Large Language Models

Large Language Models Engineer Jobs

318 jobs found

2mo ago

Sr Analytics Engineer

Northwestern Mutual Milwaukee, WI

Milwaukee, WI

2mo ago Closed

$119k - $178k/yr Full time Senior Analytics Artificial Intelligence

This position is no longer accepting applications (closed Sep 13, 2026).

Job Description

Northwestern Mutual is hiring a Sr Analytics Engineer in the Business Intelligence Center of Excellence to create reusable analytical assets and deliver trusted data products that support enterprise decision-making and AI-enabled insights. The role centers on metric certification, data lineage and metadata collection, data quality monitoring, and driving adoption of governed analytics across the organization.

Key Responsibilities

Own business and technical requirements for building reusable cross-domain analytical data products, with emphasis on metric certification, lineage identification, and metadata collection.
Partner with business stakeholders to define, document, and align metrics, KPIs, business rules, and analytical requirements to support consistency, trust, and enterprise reuse.
Build a scalable analytics layer that delivers consistent enterprise metrics (OKRs/KPIs), enabling self-service and trusted cross-functional business intelligence.
Define and maintain analytical data quality rules that connect technical standards to business requirements; proactively monitor and manage data drift.
Conduct routine data validation, reconciliation, and quality assessments across the analytics lifecycle and BICOE-supported products.
Drive cataloging of cross-domain analytical products for enterprise visibility and guidance on using assets in Atlan.
Promote adoption and reuse of analytical data products and other enterprise-approved assets.
Apply curiosity toward emerging technologies that improve data discoverability, contextual understanding, trusted analytical consumption, and AI-enabled decision support.
Collaborate with Data Solutions & Enablement and business stakeholders to ensure end-to-end alignment with enterprise data standards, governance practices, and approved data sources.
Establish a mentorship and coaching community within and beyond the BICOE.

Required Qualifications

Bachelor’s Degree in a related field.
5+ years in analytics, data, or technology-related roles, including 3+ years applying the listed technical skills in platforms such as Databricks, Snowflake, AWS, Atlan, etc.
Demonstrated understanding of Large Language Models (LLMs), including prompt engineering, model capabilities and limitations, and practical business applications.
Ability to translate ambiguous business concepts into structured metrics, analytical models, and reusable data products.
Proven capability to work cross-functionally with both business and technical stakeholders.
Experience with metric management platforms and strong collaboration skills in researching how metrics are sourced, calculated, and shared.
Strong aptitude for data quality, lineage, provenance, and governance in an analytics-focused environment.
Strong SQL skills, including query optimization and performance management.
Track record of collecting, designing, and maintaining large data models.
Experience developing large analytical datasets, semantic models, dimensional models, or curated analytical data products.
Strong understanding of modern data modeling principles, including data grain, dimensions, facts, hierarchies, and reusable business logic.

Technologies

Databricks
Snowflake
AWS
Atlan
Large Language Models (LLMs)
Prompt engineering
SQL

Skills Profile

Adaptive Communication (communicates complex service, product, system, or process details to targeted audiences and partners across technical and non-technical groups)
Business Application (translates business requirements and capabilities into technical solutions; aligns solutions to lines of business based on technology needs and business goals)
Cross Functional Partnering & Planning (coordinates collaboration across functions with different expertise)
Data Analytics (creates business knowledge through analysis and maintains data analysis data)
Data Integrity (resolves data issues to improve accuracy and consistency across the lifecycle)
Data Literacy (applies appropriate data for intended purposes; recognizes misrepresentation and escalates when needed)
Strategic Thinking (plans, designs, prioritizes, and executes high-impact initiatives)

Benefits

Flexible work schedules
Concierge service
Comprehensive benefits
Employee resource groups

Compensation

Annual salary range: USD 118,960 to 178,440.

Geographic Specific Pay Structure: Structure 110: USD 130,880.00 - 196,320.00
Geographic Specific Pay Structure: Structure 115: USD 136,800.00 - 205,200.00

Location and Work Model

Milwaukee, WI (hybrid)

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2mo ago

AI Engineer

Bain & Co. Dallas, TX

Dallas, TX

2mo ago Closed

$129k - $172k/yr Full time Backend Developer Agentic Ai API

This position is no longer accepting applications (closed Sep 12, 2026).

Job Description

Bain & Co. is hiring an AI Engineer for the Coro team to build AI-infused software and data products. This onsite role in Dallas, TX 75202 focuses on delivering LLM-driven features and agentic workflows through rapid proof-of-concepts, MVPs, and scaled enterprise deployments.

Responsibilities

Design and develop GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks.
Implement agentic workflows where they add clear value, including tool use, multi-step execution, and human-in-the-loop controls, with attention to reliability, safety, and clear failure modes.
Design and build advanced search, retrieval, and knowledge pipelines across diverse data structures and stores, including hybrid search, vector stores, and graph databases or knowledge graphs, and traditional data platforms.
Cover end-to-end retrieval pipeline needs, including indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
Build robust agent capabilities including context engineering, memory and state management (short-term and long-term), orchestration, routing, and tool integration patterns.
Integrate solutions into enterprise environments and workflows via APIs, data systems, and collaboration tools while balancing quality, latency, cost, privacy, and adoption.
Convert ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
Build ML solutions end-to-end, including data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
Apply appropriate ML methods across classical ML and deep learning, including sequence, text, and image models when relevant.
Create reproducible training and evaluation pipelines using versioning, experiment tracking, robust validation, and clear documentation.
Demonstrate fluency with deep learning fundamentals and LLM training concepts, including transformer fundamentals and pre-training versus post-training approaches such as instruction tuning and preference optimization.
Write clean, testable, maintainable code and ship AI services across the full SDLC, including build, test, deploy, monitor, and iterate.
Apply MLOps and GenAIOps practices, including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents with operational readiness.
Implement evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring when appropriate, and iteration loops for prompt and policy optimization.
Design for secure enterprise deployment using access controls, auditability, secure data handling for sensitive data and PII, and responsible AI guardrails.
Build reusable components and accelerators such as templates, evaluation harnesses, connectors, and orchestration patterns that scale across client contexts.
Communicate clearly with technical and non-technical stakeholders by leading working sessions, presenting recommendations, and writing crisp technical documentation.
Work with Bain consultants to prioritize technical decisions that unlock business value.
Support proposal shaping and scoping, including effort sizing, architecture options, risk assessment, and delivery roadmaps.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
3-5+ years of professional AI/ML engineering experience (or equivalent) with strong backend engineering fundamentals.
Strong proficiency in Python and experience building APIs/services (REST/gRPC) and integrating with enterprise systems.
Hands-on experience building LLM-powered applications with delivery considerations including latency, cost, reliability, and security.
Experience building advanced retrieval/search systems, including hybrid retrieval, vector search, and reranking, and comfort working across multiple data stores such as vector, graph, relational/document/search.
Experience implementing agentic patterns including context management, tool integration, orchestration, and memory/state handling using modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, with strong judgment on where agentic approaches are appropriate.
Experience creating reusable skills, tools, and services for agent use, including MCP, with schema validation (Pydantic) to enforce reliable data contracts.
Strong engineering practices including testing, code review, version control, CI/CD, and performance profiling.
Experience deploying and operating services on AWS, GCP, and/or Azure, including environment management, reliability, observability, and scaling.
Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production, including debugging, performance, and resilience.
Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization, access controls, PII/sensitive data handling, and enterprise risk controls.
Experience training, validating, and testing ML models with strong understanding of overfitting, generalization, and evaluation methodology.
Practical experience with feature engineering and data preprocessing for real-world datasets.
Familiarity with a broad set of ML algorithms across classical ML and deep learning and ability to select methods that match business and data constraints.
Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling such as experiment tracking, model registry, and feature store concepts.
Proven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or collaboratively.
Excellent interpersonal and communication skills, including explaining technical decisions, tradeoffs, and results to mixed audiences.
Strong stakeholder management skills and comfort working directly with clients.

Technologies

Python
REST, gRPC
LLM
LangGraph
OpenAI Agents SDK
Pydantic AI, Pydantic
MCP
AWS, GCP, Azure
Docker, Kubernetes
PyTorch, TensorFlow
CI/CD
Vector stores
Graph databases / knowledge graphs
Hybrid search, vector search, reranking
APIs
Data pipelines
MLOps, GenAIOps
SDLC

Benefits

Bain pays 100% individual employee premiums for medical, dental, and vision programs.
Generous paid time off, including parental leave, sick leave, and paid holidays.
Fully vested 401(k) company contribution.
Paid Life and Long-Term Disability insurance.

Preferred

MBA, or PhD in a technical field.
Background in consulting, professional services, or B2B analytics environments.
Experience working with major AI ecosystem partners on real client deployments.

U.S. Compensation Information

Compensation includes base salary, annual discretionary performance bonus, and a 401(k) plan with an annual employer contribution based on years of service and Bain’s benefits package.
In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California, the good-faith, reasonable annualized full-time salary range for this role is $128,500-$171,500.
Placement within the range varies based on factors including experience, education, licensure/certifications, training, and skill level.
Annual discretionary performance bonus and additional elements of discretionary compensation may apply.
401(k) company contribution: 4.5%, increasing after 3 years of service and 100% vested upon start date.
For all other locations, the good-faith, reasonable annualized full-time salary range is commensurate with competitive geographic market rates and varies based on factors including experience, education, licensure/certifications, training, and skill level.
In Massachusetts, it is unlawful to require or administer a lie detector test as a condition of employment or continued employment; violations may result in criminal penalties and civil liability.

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2mo ago

AI Engineer

Bain & Co. New York, NY

New York, NY

2mo ago Closed

$129k - $172k/yr Full time Backend Developer Agentic Ai AI

This position is no longer accepting applications (closed Sep 10, 2026).

Job Description

The AI Engineer role at Bain’s Coro team focuses on building AI-infused software and data products, including LLM-driven features and agentic workflows that move from proof of concept through scaled deployments.

Location

New York, NY (onsite)

Compensation

USD 128,500 - 171,500 per yearly. Compensation includes base salary, an annual discretionary performance bonus, and a 401(k) plan with an annual employer contribution as described below, along with Bain’s best-in-class benefits package.

In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California, the good-faith, reasonable annualized full-time salary range is $128,500-$171,500.
Placement within this range varies based on experience, education, licensure/certifications, training, and skill level.
The role may also be eligible for other elements of discretionary compensation.

Role Summary

Within Bain’s Coro team, you will build LLM-enabled applications and workflows that support commercial teams, including retrieval and knowledge pipelines, MLOps and GenAIOps practices, and secure enterprise integration. Work can include POCs, MVPs, and production-scale deployments.

Key Responsibilities

Design and develop GenAI applications (for example, copilots, workflow automation, decision support) using modern LLM stacks.
Implement agentic workflows when they add clear value, including tool use, multi-step execution, and human-in-the-loop controls, with focus on reliability, safety, and clear failure modes.
Design and build advanced search, retrieval, and knowledge pipelines across diverse data structures and stores, including hybrid search, vector stores, and graph databases or knowledge graphs; cover indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
Build robust agent capabilities including context engineering, short-term and long-term memory and state management, orchestration, routing, and tool integration patterns.
Integrate solutions into enterprise environments and workflows via APIs and data systems and collaboration tools, balancing quality, latency, cost, privacy, and adoption.
Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
Build ML solutions end-to-end, including data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
Apply appropriate methods across classical ML and deep learning (including sequence, text, and image models when relevant).
Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and documentation.
Demonstrate fluency with modern deep learning and LLM concepts, including transformer fundamentals and LLM pre-training versus post-training (such as instruction tuning and preference optimization approaches).
Write clean, testable, maintainable code and deliver AI services through the full SDLC: build, test, deploy, monitor, and iterate.
Implement MLOps and GenAIOps practices including CI/CD, reproducibility, environment parity, and model/prompt/agent versioning, with operational readiness.
Build evaluation and observability for GenAI and agentic systems, including tracing and instrumentation, regression test suites, automated scoring when appropriate, and iteration loops for prompt and policy optimization.
Design for secure enterprise deployment with access controls, auditability, and data handling for sensitive and PII data, plus responsible AI guardrails.
Create reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.
Communicate with technical and non-technical stakeholders, lead working sessions, present recommendations, and write crisp technical documentation.
Work with Bain consultants to prioritize technical decisions that unlock business value.
Support proposal shaping and scoping, including effort sizing, architecture options, risk assessment, and delivery roadmaps.

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
3-5+ years of professional AI/ML engineering experience (or equivalent), with strong backend engineering fundamentals.
Strong proficiency in Python, experience building APIs/services (REST/gRPC), and integrating with enterprise systems.
Hands-on experience building LLM-powered applications, with delivery considerations including latency, cost, reliability, and security.
Experience building advanced retrieval/search systems such as hybrid retrieval, vector search, and reranking, and comfort working across multiple data stores (vector, graph, relational/document/search).
Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) using modern frameworks such as LangGraph, OpenAI Agents SDK, or Pydantic AI, or custom agent loops, with judgment on where agentic approaches apply.
Experience creating reusable skills/tools/services for agent use, including MCP, and schema validation (such as Pydantic) to enforce reliable data contracts.
Strong engineering practices including testing, code review, version control, CI/CD, and performance profiling.
Experience deploying and operating services on AWS, GCP, and/or Azure, including environment management, reliability, observability, and scaling.
Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production, including debugging, performance, and resilience.
Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization, access controls, PII/sensitive data handling, and enterprise risk controls.
Experience training, validating, and testing ML models, with understanding of overfitting, generalization, and evaluation methodology.
Practical experience with feature engineering and data preprocessing for real-world datasets.
Familiarity with a broad set of ML algorithms (classical ML and deep learning) and selecting methods matching business and data constraints.
Familiarity with deep learning frameworks such as PyTorch/TensorFlow and ML lifecycle tooling such as experiment tracking and model registry, plus feature store concepts.
Ability to work in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team.
Excellent interpersonal and communication skills to explain technical decisions, tradeoffs, and results to mixed audiences.
Strong stakeholder management skills and comfort working directly with clients.

Technologies

English
Python
REST, gRPC
LLM stacks, LangGraph
OpenAI Agents SDK, Pydantic AI, Pydantic
MCP
AWS, GCP, Azure
Docker, Kubernetes
Vector stores, graph databases, knowledge graphs
Hybrid retrieval, vector search, reranking
Transformer fundamentals
Instruction tuning, preference optimization approaches
PyTorch, TensorFlow
CI/CD
APIs, data pipelines

Benefits

Medical, dental, and vision programs (100% individual employee premiums).
Generous paid time off, including parental leave, sick leave, and paid holidays.
Fully vested 401(k) company contribution.
Paid Life and Long-Term Disability insurance.
Annual fitness reimbursements.
401(k) plan with an annual employer contribution based on years of service: 4.5% 401(k) company contribution, increasing after 3 years of service, and 100% vested upon start date.
Annual discretionary performance bonus.

Preferred Qualifications

MBA or PhD in a technical field.
Background in consulting, professional services, or B2B analytics environments.
Experience working with major AI ecosystem partners on real client deployments.

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2mo ago

AI Engineer

Bain & Co. Chicago, IL

Chicago, IL

2mo ago Closed

$129k - $172k/yr Full time Backend Developer Agentic Ai AI

This position is no longer accepting applications (closed Sep 10, 2026).

Job Description

Bain’s Commercial Excellence practice helps B2B clients turn market opportunity into booked revenue through sharper go-to-market strategy, improved sales coverage and capacity, and industrialized execution. Within Bain, the Coro business unit builds software and data solutions that are an increasingly critical part of that approach. As part of Coro, you will contribute to the next generation of Coro products with AI and agentic capabilities, including LLM-driven features and retrieval and knowledge pipelines designed for secure enterprise deployment.

This AI Engineer role is based in Chicago, IL (onsite) with a salary range of USD 128,500 - 171,500 per year. The team focuses on building reliable, production-ready AI services that balance quality, latency, cost, privacy, and adoption.

Responsibilities

Design and develop GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks.
Implement agentic workflows where they create clear value, including tool use, multi-step execution, and human-in-the-loop controls, with emphasis on reliability, safety, and clear failure modes.
Design and build advanced retrieval, search, and knowledge pipelines across diverse data structures and stores, including hybrid search, vector stores, and graph databases or knowledge graphs. Cover indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
Build robust agent capabilities including context engineering, short-term and long-term memory and state management, orchestration, routing, and tool integration patterns.
Integrate solutions into enterprise environments and workflows through APIs, data systems, and collaboration tools, managing tradeoffs across quality, latency, cost, privacy, and adoption.
Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
Build ML solutions end-to-end, including data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
Choose appropriate methods across classical ML and deep learning, including sequence, text, and image models when relevant.
Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and clear documentation.
Ship AI services across the full SDLC: build, test, deploy, monitor, and iterate.
Apply MLOps and GenAIOps practices including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents.
Implement evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization.
Design for secure enterprise deployment with access controls, auditability, appropriate data handling for sensitive and PII data, and responsible AI guardrails.
Create reusable components and accelerators such as templates, evaluation harnesses, connectors, and orchestration patterns that scale across client contexts.
Communicate with technical and non-technical stakeholders by leading working sessions, presenting recommendations, and writing crisp technical documentation.
Partner with Bain consultants to prioritize critical technical decisions that unlock business value.
Support proposal shaping and scoping including effort sizing, architecture options, risk assessment, and delivery roadmaps.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
3-5+ years of professional AI / ML engineering experience (or equivalent), plus strong backend engineering fundamentals.
Strong proficiency in Python and experience building APIs/services (REST/gRPC) and integrating with enterprise systems.
Hands-on experience building LLM-powered applications with delivery considerations including latency, cost, reliability, and security.
Experience building advanced retrieval/search systems (hybrid retrieval, vector search, reranking) and comfort working across multiple data stores including vector, graph, relational/document/search.
Experience implementing agentic patterns for context management, tool integration, orchestration, and memory/state handling, using modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, with judgment on when agentic approaches are and are not appropriate.
Experience creating reusable skills, tools, and services for agent use, including MCP, with schema validation (Pydantic) for reliable data contracts.
Strong engineering practices including testing, code review, version control, CI/CD, and performance profiling.
Experience deploying and operating services on AWS, GCP, and / or Azure with environment management, reliability, observability, and scaling.
Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production.
Proven ability to implement security, privacy, and governance for AI systems including authentication/authorization, access controls, and PII/sensitive data handling.
Experience training, validating, and testing ML models, including understanding of overfitting, generalization, and evaluation methodology.
Practical experience with feature engineering and data preprocessing for real-world datasets.
Familiarity with a broad set of ML algorithms (classical ML and deep learning) and ability to choose methods aligned to business and data constraints.
Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling such as experiment tracking and model registry.
Ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team.
Excellent communication skills for explaining technical decisions and tradeoffs to mixed audiences.
Strong stakeholder management skills and comfort working directly with clients.

Technologies

Python, REST, gRPC
LLM, LangGraph, OpenAI Agents SDK, Pydantic AI, Pydantic, MCP
AWS, GCP, Azure
Docker, Kubernetes
PyTorch, TensorFlow
CI / CD, vector stores, graph databases, knowledge graphs

Benefits

Medical, dental and vision programs (Bain pays 100% of individual employee premiums).
Generous paid time off, including parental leave, sick leave and paid holidays.
Fully vested 401(k) company contribution.
Paid Life and Long-Term Disability insurance.
Annual fitness reimbursements.
401(k) company contribution: 4.5% company contribution that increases after 3 years of service and is 100% vested upon start date.
Annual discretionary performance bonus.

About Coro and the Commercial Excellence Practice

Bain’s B2B Commercial Excellence practice develops a clear go-to-market strategy by building market understanding, designing sales coverage and capacity models, and creating an industrialized sales execution capability.
Coro’s software and data solutions support this approach.
Coro℠ is a business unit bringing together Bain’s proprietary SaaS and DaaS tools, including cloud-based software, online capability assessments, and advanced analytics focused on enabling Commercial Excellence for B2B companies.

Preferred

MBA, or PhD in a technical field.
Background in consulting, professional services, or B2B analytics environments.
Experience working with major AI ecosystem partners on real client deployments.

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2mo ago

Machine Learning Engineer

Speria Atlanta, GA

Atlanta, GA

2mo ago Closed

Full time Agentic Ai Ai Ml Artificial Intelligence

This position is no longer accepting applications (closed Sep 9, 2026).

Job Description

Speria MTech offers the chance to build machine learning and optimization solutions that support real-time decision-making. This role in Atlanta, GA (hybrid) focuses on taking operational data and turning it into predictions and automated actions, with production deployment, monitoring, and continuous improvement built into the work.

What you will do

Design and build machine learning and optimization pipelines that support decisioning in production
Develop and deploy production-grade models for operational use
Create feature pipelines using distributed systems such as Spark
Collaborate with data engineering to support data readiness for modeling and deployment
Develop decisioning systems that drive automated actions
Monitor model performance and improve continuously after deployment

What you bring

Bachelor’s or Master’s degree in a relevant field (MS may qualify with 1–2 years; BS typically 4–6 years)
2–4 years experience in Machine Learning or optimization
Experience deploying machine learning systems in production
Familiarity with an end-to-end machine learning flow, including Databricks and CI/CD
Strong Python programming at production level
Experience with ML frameworks including PyTorch, TensorFlow, and scikit-learn

Tools and technologies you will work with

Python, PyTorch, TensorFlow, scikit-learn
Spark, Databricks
CI/CD, Git
SQL
LLMs, generative AI, and agent-based systems

Preferred skills

SQL and Apache Spark experience
Feature engineering pipelines for machine learning
Git, CI/CD, and reproducible workflows
Experience with real-time systems
Familiarity with LLMs, generative AI, and agent-based systems
MS may qualify with 1–2 years; BS typically 4–6 years

Location: Atlanta, GA (hybrid)

Minimum experience: 2 years

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2mo ago

Sr. Machine Learning Engineer

AppFolio Atlanta, GA

Atlanta, GA

2mo ago Closed

$167k - $209k/yr Full time Senior Agentic Ai Artificial Intelligence

This position is no longer accepting applications (closed Sep 8, 2026).

Job Description

AppFolio is seeking a Senior Machine Learning Engineer to design and ship voice and conversational AI agents within Realm-X. This role will define production-grade voice and chat agent pipelines, with a focus on balancing LLM reasoning quality against low-latency streaming across multi-channel customer experiences.

Responsibilities

Architect and deliver voice and text agent pipelines for real-time, multi-turn customer interactions.
Evaluate and implement principled trade-offs between reasoning depth and latency using frontier LLMs, smaller models, and routing strategies.
Lead a small pod of ML and platform engineers, strengthening standards for agent evaluation, observability, and incident response.
Collaborate with Product and Voice channel teams to define KPIs, build evaluation harnesses, and establish agent acceptance criteria focused on quality.
Drive selective Small Language Model (SLM) fine-tuning and inference optimization to improve voice latency and control cost.

Requirements

Deep, production experience with LangChain, LangGraph, LangSmith, and LangChain Deep Agents (or equivalent agent frameworks).
Hands-on experience with voice systems, including Voice-to-Voice models and traditional TTS/STT pipelines, with clear understanding of end-to-end voice models versus modular STT LLM TTS architectures.
Strong knowledge of LLM reasoning behavior, tool use, structured output, and reasoning-versus-latency trade-offs across model providers.
Production experience with Twilio (or comparable telephony) and AWS.
Expertise in Python, async programming, and WebSockets to support real-time, bidirectional streaming.
Solid foundation in deep learning, model evaluation, and inference optimization, including the ability to deploy with Docker on AWS.
Proven ability to lead a small team, mentor engineers, and partner credibly with Product and Design.
Demonstrated experience shipping production AI agents serving real users in voice and/or text channels.
Systems thinking, with the ability to reason about pipelines and end-to-end behavior beyond model development.
Ability to move quickly while maintaining sound engineering judgment.
Collaborative, low-ego working style, focused on elevating teammates.
Values work-life balance as a foundation for sustained high performance.

Technologies

LangChain, LangGraph, LangSmith, LangChain Deep Agents
Voice-to-Voice models, TTS, STT
Twilio, AWS
Python, async programming, WebSockets
Docker
LLM, Small Language Model (SLM)
RAG

Benefits

Regular full-time employees are eligible for benefits
#LI-KB1

Nice to Have

Experience fine-tuning Small Language Models for domain-specific voice applications.
Familiarity with RAG over structured business data and tool-using agents over API surfaces.
Prior experience in regulated or customer-facing industries with strict reliability requirements.
Publicly verifiable work on GitHub, in open-source agent frameworks, or in community competitions.

Location

Atlanta, GA (onsite)

Find out more about our locations by visiting our site.

Compensation & Benefits

The compensation that we reasonably expect to pay for this role is USD 167,200 - 209,000 base pay.
The actual compensation will be determined by factors including skills, education, experience, and internal equity.
Compensation is one component of a comprehensive Total Rewards package.
The compensation range does not include additional benefits or any discretionary bonuses that may be available based on role and/or employment type.

Regular full-time employees are eligible for benefits - see here.

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2mo ago

Senior Applied AI Engineer

Veracity Insurance Solutions Remote 🌍 Remote

Remote

2mo ago Closed

$140k - $170k/yr Full time Backend Developer Senior Agentic Ai

This position is no longer accepting applications (closed Sep 8, 2026).

Job Description

Veracity Insurance Solutions is building an API/MCP-first insurance platform for a regulated domain, and it needs a Senior Applied AI Engineer to bring production-grade agent systems to life. This is a senior individual contributor role with strong architectural influence, focused on hardening agentic workflows and helping define AI-first engineering and product principles across teams.

As a remote engineer, you will design and ship agentic capabilities that integrate with the platform’s deterministic core, while establishing reference patterns others can adopt. The role also includes shaping reliability and evaluation practices so agent behavior is measurable and defensible.

Responsibilities

Design and ship production agentic systems, including multi-agent orchestration, tool use, RAG pipelines, and MCP-based integrations against platform APIs
Build a reliability layer around agents using evals, guardrails, observability, cost controls, and regression testing to ensure agent behavior is measurable
Implement maker-checker patterns so AI accelerates regulated workflows without autonomously touching money, bind, or compliance decisions
Integrate agent capabilities with the deterministic core, including a canonical data model, Config Hub, and Control Tower task orchestration
Co-author and implement AI-first engineering principles such as agent harnesses, AI-assisted code review, shared skills and prompt libraries, eval-gated CI, and dev-environment standards, then drive adoption across squads in Vilnius and the US
Shape AI-first product principles, including where AI belongs in the product and where it does not, human-in-the-loop patterns, agent UX conventions, and how AI capability estimates influence scoping and build-versus-buy decisions
Set the standard through your own delivery by demonstrating what AI-first engineering enables and establishing reference patterns for other engineers
Work with engineering leadership on the AI architecture roadmap and year-end production proof points
Perform other duties as requested, directed, or assigned

Requirements

5+ years of software engineering experience with strong backend or full-stack foundations, including APIs, event-driven systems, and data modeling
Real production experience with LLM-powered systems, including agent orchestration, tool calling, RAG, and structured outputs (not only prototypes or demos)
Demonstrated ability to make agentic systems reliable, including evals, fallbacks, deterministic checkpoints, and cost and latency management
Strong product judgment, scoping AI where it earns its place and defaulting to deterministic code where it does not
Evidence of changing how others build, such as workflows, harnesses, or practices that were introduced and kept using
Clear written communication and comfort operating in an async-first, distributed team environment