Ai Agent Systems

Ai Agent Systems Engineer Jobs

15 jobs found

Today

Sr. Machine Learning Engineer

AppFolio Inc Atlanta, GA

Atlanta, GA

Today

$167k - $230k/yr Full time Agentic Ai Platform Ai Agent Platform Ai Agent Systems

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Job Description

AppFolio is hiring a Senior Machine Learning Engineer to design and ship production voice and conversational AI agents within the Realm-X AI-native platform.

Responsibilities

Architect and ship voice and text agent pipelines for real-time, multi-turn customer interactions
Make principled trade-offs between reasoning depth and latency across frontier LLMs, smaller models, and routing strategies
Lead a small pod of ML and platform engineers; improve agent evaluation, observability, and incident response
Partner with Product and Voice channel teams to define KPIs, eval harnesses, and acceptance criteria for agent quality
Drive selective SLM fine-tuning and inference optimization to reduce voice latency and cost
Turn cutting-edge agent and voice research into reliable, low-latency, multi-channel experiences at scale across AppFolio’s customer base

Requirements

5+ years building and scaling web-based applications, ideally in a SaaS environment
Shipped production AI agents serving real users in voice and/or text channels
Systems mindset: think in pipelines and systems, not only models
Sound engineering judgment
Humility, collaboration, and low-ego approach; ability to elevate others
Values work-life balance as a basis for sustained performance
Deep, shipped experience with LangChain, LangGraph, LangSmith, and LangChain Deep Agents (or equivalent agent frameworks)
Hands-on with Voice-to-Voice models and traditional TTS/STT pipelines, including trade-offs between end-to-end voice models and modular STT + LLM + TTS architectures
Strong understanding of LLM reasoning behavior, tool use, structured output, and reasoning-vs-latency trade-offs across providers
Production experience with Twilio (or comparable telephony) and AWS
Expert Python, async programming, and WebSockets for real-time bidirectional streaming
Foundation in deep learning, model evaluation, and inference optimization; deploy with Docker on AWS
Experience leading a small team, mentoring engineers, and partnering credibly with Product and Design

Technologies

Python
Async programming
WebSockets
LangChain
LangGraph
LangSmith
LangChain Deep Agents
Voice-to-Voice models
TTS
STT
LLM
Small Language Models (SLM)
Twilio
AWS
Docker
Real-time, bidirectional streaming

Nice to Have

Bachelor’s, Master’s, or Ph.D. in Computer Science or a related technical discipline
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
Experience in regulated or customer-facing industries with strict reliability requirements
Publicly verifiable work on GitHub, open-source agent frameworks, or community competitions

Location

Atlanta, GA (onsite)
Find out more about locations by visiting the company site
All late-stage candidates complete an in-person meeting with an AppFolian as part of the hiring process

Compensation & Benefits

Base pay: $167,200 - $229,900 per year
Actual compensation depends on factors including skills, education, experience, and internal equity
Regular full-time employees are eligible for benefits (details available via the provided link)

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2d ago

Senior Software Engineer, Product

Decagon New York, NY

New York, NY

2d ago

$200k - $400k/yr Full time Backend Developer Python Senior

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Job Description

Build customer-facing systems that help teams configure, train, test, and monitor sophisticated AI agents in production.

Responsibilities

Reduce manual effort by delivering self-serve functionality for agent configuration, training, and deployment
Create configuration abstractions that support both enterprise use cases and non-technical usability
Develop AI-powered tooling enabling CX and ops teams to create and manage complex agent workflows without writing code
Implement monitoring, tracing, and analytics that highlight actionable failure signals, including where an agent fails and why
Work on agentic systems at scale, contributing to critical production flows for Fortune 100 companies
Run evaluations on new text and voice models, then integrate winning candidates into production traffic

Requirements

4+ years building software products with strong product thinking
Proficiency in Python and comfort working across the entire stack
Ability to turn complex technical capabilities into simple, usable interfaces
Complex systems instincts, including asynchronous programming and understanding failure modes that show up at scale
Strong debugging skills for system failures in deep technology stacks, using the right tools to reach the answer
Skill in translating customer requests into clear, well-scoped product and technical specifications
Good judgment on when to ship quickly versus when to slow down and build correctly

Technologies

Python

Benefits

Medical, Dental, and Vision benefits for you and your family
Life Insurance and Disability Benefits
Retirement Plan (e.g., 401K, pension)
Parental Leave
Fertility and family building benefits through Carrot
Monthly stipend to support your wellness, lifestyle, and work-life balance
Daily lunches and snacks in the office
Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)

Compensation

$200,000 - $400,000 per year + offers equity
Compensation range depends on experience, skills, and scope of responsibilities
Final compensation may vary based on location within the United States

Additional background (Even Better)

Experience with LLMs, prompt engineering, evals, or AI agent systems
Experience building developer tools, APIs, platforms, or internal tools that became customer-facing
Experience with observability, data visualization, or analytics products
Experience building security-sensitive systems such as authentication and data access controls
Experience working with multi-modal or voice models

Location: New York, NY (onsite)

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2d ago

Senior Software Engineer AI

Thomson Reuters Eagan, MN

Eagan, MN

2d ago

$100k - $204k/yr Full time Backend Developer Senior Ai Agent Systems

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Job Description

Senior AI Engineer role on Thomson Reuters’ agent platform team, building and deploying AI agent systems end-to-end using AI-native engineering practices.

Responsibilities

Design and deliver complex AI agent systems end-to-end, including multi-component pipelines, tool integrations, and multi-step reasoning for initiatives you own
Build production AI agent systems using modern LLM APIs, agent SDKs, and orchestration/runtime infrastructure; integrate cleanly with existing platform services
Advance agent observability and evaluation through tracing, automated evals, and quality or drift detection to catch regressions before they reach customers
Apply and extend progressive delivery practices such as feature-flagged rollout and earlier-stage quality signals to improve release velocity and stability
Participate in cross-cutting design discussions and technical decisions via RFCs/ADRs, ensuring new systems fit into the overall architecture
Mentor engineers and help build technical culture by modeling effective AI-native, agentic development practices
Collaborate with product and engineering peers to translate requirements into solid technical designs

Requirements

Proven experience architecting and shipping production software systems, with hands-on work building AI/LLM-powered products or agentic systems (not limited to classical ML/data science)
Direct experience building with modern LLM APIs, agent frameworks/SDKs, or agent-to-tool integration protocols
Ability to own complex technical projects end-to-end, from design through production delivery
Strong backend engineering foundation: distributed systems design, service architecture, and cloud infrastructure
Experience with production reliability and observability: monitoring, evaluation pipelines, incident response, and quality regression detection
Ability to mentor other engineers and contribute to a strong technical culture
Comfort operating with high autonomy on a senior-heavy, fast-moving team using AI-native/agentic engineering practices

Technologies

LLM APIs
Agent SDKs
Orchestration/runtime infrastructure
Feature flagging
Tracing
Automated evals
Monitoring
AWS Bedrock AgentCore

Location and Salary

Location: Eagan, MN (hybrid)
Salary range: USD 100,000 - 204,200 per year

Benefits

Hybrid Work Model
Flex My Way: flexible hybrid working, work from anywhere for up to 8 weeks per year
Grow My Way: programming and skills-first approach
Comprehensive benefit plans including flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing
Social Impact Institute: two paid volunteer days off annually, plus opportunities for pro-bono consulting and ESG initiatives
US benefits package including market competitive health, dental, vision, disability, and life insurance; competitive 401k plan with company match
Paid time off including paid holidays (including two company mental health days off), parental leave, sabbatical leave
Optional hospital, accident and sickness insurance paid 100% by the employee
Optional life and AD&D insurance paid 100% by the employee
Flexible Spending and Health Savings Accounts
Fitness reimbursement
Access to Employee Assistance Program
Group Legal Identity Theft Protection benefit paid 100% by employee
Access to 529 Plan
Adoption & Surrogacy Assistance
Access to Employee Stock Purchase Plan
Ability to receive an Annual Bonus based on a combination of enterprise and individual performance

Preferred Qualifications

Experience with developer-facing platforms, plugin/extension systems, or integration ecosystems
Familiarity with progressive delivery practices (feature flagging, phased rollout, blue/green deployment) at scale
Experience with managed agent runtime infrastructure (for example, AWS Bedrock AgentCore or similar)
Domain expertise in legal, regulated, professional-services, or other fiduciary domains is a plus, but not required

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3d ago

Staff AI Engineer

Securitas Security Services Elmsford, NY

Elmsford, NY

3d ago

$155k - $180k/yr Full time Agentic Ai AI Ai Agent

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Job Description

Staff AI Engineer opportunity at Securitas Security Services to lead agentic AI platform development and integration across a cloud-native Azure stack.

Responsibilities

Lead hands-on development of an agentic AI platform, with emphasis on zero-trust security, scalability, and performance in Azure
Build and deploy new agents that connect to third-party enterprise vendors (including workforce management and HR platforms) to enable advanced capabilities
Design secure, reusable agent templates, orchestration workflows, and connectors for Model Context Protocol (MCP) and Agent 2 Agent (A2A)
Define strict Bring Your Own Agent (BYOA) standards, with automated testing and CI/CD release workflows in partnership with QA and DevOps
Mentor and perform code reviews for internal engineers and contractor teams to maintain technical excellence and enforce AI security best practices
Use GenAI and developer automation tools to optimize workflows and improve execution speed

Requirements

Minimum 8 years of software engineering experience
Deep proficiency in Python and modern API development
Proven success designing, deploying, and observing LLM-powered applications, including orchestrated multi-agent workflows using LangGraph and scalable RAG systems
Hands-on experience building within cloud-native Azure architectures such as Azure AI Foundry, Container Apps, Cosmos DB, and Key Vault
Experience creating robust API integrations, external system connectors, and/or connectors for MCP and A2A
Strong understanding of AI observability tools such as Langfuse (latency, token usage, control-flow decisions, session analytics)
Track record of shipping high-impact features quickly while managing technical debt pragmatically
Technical initiative: proactively proposes solutions and architectures
Low-ego collaboration: adapts initial designs based on team feedback

Technologies

Python
LangGraph
Langfuse
Model Context Protocol (MCP)
Agent 2 Agent (A2A)
GenAI
Azure
Azure AI Foundry
Container Apps
Cosmos DB
Key Vault
CI/CD

Benefits

Competitive pay
Weekly pay
Comprehensive benefits for eligible employees
Paid training and professional development
Career advancement opportunities
Employee recognition programs
Supportive and inclusive work environment
Opportunity to work for the world’s leading protective services company
Medical, dental, and vision insurance
Life and disability insurance
Paid time off
401(k) or retirement savings plan
Employee assistance program
Additional voluntary benefits, where applicable

Location and Work Setup

Elmsford, NY (hybrid)
Hybrid role: 2 days a week if within commutable distance, or fully remote
Minimal travel required (up to 2 domestic trips per year)

Compensation

Pay range: $155,000 - $180,000 per year
Actual compensation depends on experience, qualifications, work location, applicable laws, and business needs

Reasonable Accommodation

Securitas USA is committed to inclusion and will provide reasonable accommodations for qualified individuals with disabilities. For accommodations related to the application or interview process, contact PrivacyRequests@securitasinc.com.

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3d ago

AI Engineer

Oakwood Systems Group Inc. St. Louis, MO

St. Louis, MO

3d ago

Full time Backend Developer Agentic Ai Ai Agent

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Job Description

Oakwood Systems Group Inc. seeks an experienced, hands-on AI Engineer to deliver secure, production-ready generative AI and agentic solutions on the Microsoft cloud.

Responsibilities

Design, build, and deploy production AI applications, copilots, and agents using Microsoft Azure and modern AI frameworks.
Build RAG and enterprise knowledge solutions using embeddings, vector search, document processing, metadata, and retrieval strategies for grounded, source-aware answers.
Design agentic workflows using tools, APIs, structured outputs, memory/state, human approval, and multi-step orchestration to automate business processes safely.
Integrate AI capabilities with enterprise applications and data sources via REST APIs, Microsoft Graph, Power Platform, databases, SaaS platforms, and custom services.
Develop maintainable backend services and APIs, primarily in Python, including testing, error handling, logging, security, and performance considerations.
Define and apply evaluation approaches for AI quality, groundedness, reliability, safety, latency, and cost; use results to improve prompts, retrieval, orchestration, and model selection.
Apply production engineering practices: source control, automated testing, CI/CD, containerization, secrets management, observability, and environment promotion.
Implement enterprise security and governance practices, including identity, role-based access, data boundaries, auditability, responsible AI controls, and human-in-the-loop patterns where appropriate.
Partner with client business and technical stakeholders to clarify requirements, prototype solutions, explain tradeoffs, and translate AI possibilities into deployable designs.
Support new client opportunities through architecture reviews, code reviews, estimation, technical discovery, and solution design.
Create reusable patterns, accelerators, reference implementations, and delivery assets to improve speed and consistency across engagements.
Document solutions and support knowledge transfer so client teams can operate and extend delivered capabilities.
Mentor less-experienced engineers and help establish pragmatic engineering standards for production AI delivery.

Requirements

5+ years professional engineering experience across software engineering, machine learning engineering, data engineering, applied AI, backend development, or a closely related discipline, with meaningful ownership of production systems.
2+ years hands-on experience building generative AI / LLM-powered applications or AI-enabled software, including at least one solution moved beyond prototype into a production or production-like environment.
Strong Python skills and experience building production services, APIs, integrations, or data/AI pipelines.
Hands-on experience with LLM application patterns: prompt design, RAG, embeddings, vector search, structured outputs, function/tool calling, and agent orchestration.
Experience evaluating AI systems using measurable criteria (not only subjective demos), including quality, reliability, safety, latency, and/or cost.
Cloud-based application experience with strong Azure experience preferred; ability to work effectively in a Microsoft-centric environment.
Working knowledge of SQL and enterprise data integration, including reasoning about data quality, access, lineage, and how data architecture impacts AI outcomes.
Modern engineering practices: Git, peer review, automated testing, CI/CD, containers, APIs, logging/monitoring, and secure configuration/secrets management.
Ability to troubleshoot production AI behavior including retrieval failures, hallucinations, tool-calling errors, prompt regressions, latency, cost, and edge cases.
Strong client-facing communication to explain technical concepts, architecture decisions, risks, and tradeoffs to engineers and business stakeholders.
Ability to work independently in a consulting environment, manage competing priorities, and move from incomplete requirements to a well-structured implementation plan.
Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent practical experience.

Technologies

Microsoft Azure, Azure AI Search, Azure OpenAI, Azure AI Foundry
Python, embeddings, vector search, document processing, structured outputs
Function/tool calling, agent orchestration (Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI)
REST APIs, Microsoft Graph, Power Platform, Power Automate
Databases, SQL
Git, CI/CD, containers, logging/monitoring, secrets management, observability
Identity, role-based access, human-in-the-loop, Azure Functions, Container Apps, App Service, AKS, API Management
Cosmos DB, Azure SQL, Key Vault, Entra ID
MLOps, LLMOps, Microsoft Fabric, Databricks
Multimodal models, fine-tuning, model serving
GitHub Copilot, Claude Code, Codex, Cursor, Model Context Protocol (MCP)
Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI, Model Context Protocol (MCP)
Microsoft Fabric

Preferred Qualifications

Hands-on experience with Microsoft Foundry / Azure AI Foundry, Azure OpenAI, Azure AI Search, Copilot Studio, Power Automate, or related Microsoft AI services.
Experience with orchestration frameworks such as Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI, or custom agent frameworks.
Experience with Model Context Protocol (MCP), agent-to-tool integration patterns, or reusable enterprise tool registries.
Experience with Azure application services including Functions, Container Apps, App Service, AKS, API Management, Cosmos DB, Azure SQL, Key Vault, and Entra ID.
Experience with MLOps / LLMOps practices such as evaluation pipelines, model or prompt versioning, tracing, monitoring, feedback loops, and production incident response.
Experience with Microsoft Fabric, Databricks, or other modern data platforms for governed enterprise data preparation for AI.
Experience with classical machine learning, NLP, deep learning, fine-tuning, multimodal models, or model serving when foundation-model APIs are not sufficient.
Experience using AI-assisted development tools (GitHub Copilot, Claude Code, Codex, Cursor) with disciplined engineering review for correctness, security, and maintainability.
Prior consulting, professional services, systems integration, or Microsoft partner experience.
Relevant Microsoft or cloud certifications (Azure AI Engineer, Azure Developer, Azure Solutions Architect, or equivalent).

What Success Looks Like

AI solutions progress from discovery and prototype to secure, supportable production deployments with clear acceptance criteria.
Clients can articulate business value beyond technical novelty.
AI quality improves through repeatable evaluation, observability, and feedback rather than one-time demonstrations.
Solutions integrate with client applications, data, identity, and operating processes and remain maintainable after handoff.
Reusable engineering patterns and delivery assets reduce effort and risk on future engagements.
Trusted delivery that combines technical depth, sound judgment, and practical execution.

Why Oakwood

Deep Microsoft expertise combined with hands-on application, data, cloud, and AI engineering.
Microsoft Solutions Partner designations across key solution areas and an Advanced Specialization for AI Applications on Microsoft Azure.
AI Engineers work directly with clients, build production solutions, shape reusable delivery patterns, and help organizations turn rapidly evolving AI capabilities into durable business outcomes.

Location and Experience

Location: St. Louis, MO (onsite)
Experience: 5+ years

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3d ago

Member of Technical Staff – Forward Deployed AI Engineer

MaxIT Consulting - Max Corporate Group San Francisco, CA

San Francisco, CA

3d ago

Full time Ai Agent Engineer Ai Agent Systems Artificial Intelligence

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Job Description

MaxIT Consulting - Max Corporate Group is building forward-deployed AI capabilities that connect applied research judgment with software engineering delivery. In this role, you will help design, implement, and bring advanced agent systems into production for enterprise workflows, partnering closely with customer researchers and engineers throughout the journey from requirements to real-world use.

What you will do

Build and ship production agent systems for complex enterprise workflows.
Develop data pipelines, orchestration systems, and evaluation harnesses.
Run and analyze post-training workflows for customer-specific models and agents.
Work directly with customer researchers and engineers through implementation and production use.
Assess whether performance issues originate in the model, environment, or evaluation framework.
Translate customer workflows into robust, testable AI systems.
Own technical deployments from implementation through production use.

What you bring

Strong software engineering fundamentals and demonstrated ability to ship production systems.
Hands-on experience building agent systems, AI applications, or other technical forward-deployed solutions.
Ability to personally write and own production code.
Strong understanding of evaluation, agent behavior, and production reliability.
Ability to work across technologies and learn new stacks as required by the problem.
Strong communication skills with technical enterprise stakeholders.

Location and work arrangement

The team is based in San Francisco, CA and the role is primarily on-site. Limited flexibility may be considered for exceptional candidates, particularly experienced candidates elsewhere in the United States who can spend an initial period working on site.

Seniority

The search is open from exceptional new graduates and early-career engineers through experienced senior engineers.
Technical ability and demonstrated ownership are more important than years of experience.

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4d ago

Senior Applied AI Engineer

Microsoft Redmond, WA

Redmond, WA

4d ago

$120k - $261k/yr Full time Senior Agentic Ai Ai Agent

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Job Description

The Senior Applied AI Engineer role at Microsoft (ACES CØDE) focuses on designing, building, and operating production agentic AI services that improve AI-first support experiences at Azure scale.

Responsibilities

Analyze data signals to identify patterns across support-case trends, customer issue patterns, and authorized technical signals at scale, uncovering recurring failure patterns and gaps in Azure diagnostics and documentation.
Develop scalable agentic AI solutions for Azure diagnostics and documentation using LLM orchestration, RAG pipelines, agent workflows, tool integrations, and memory and state management, including human-in-the-loop experiences.
Write, review, test, and maintain production-quality C# and Python code.
Create reusable components, reference implementations, and engineering patterns that accelerate delivery across teams.
Translate customer support scenarios and product requirements into technical designs, then deliver end-to-end implementation, testing, deployment, monitoring, and continuous improvement.
Develop evaluation frameworks using rubrics, golden datasets, simulations, judge models, and offline and online evaluation.
Use measurable quality thresholds to transition agent capabilities from experimentation into production operations.
Ensure services meet requirements for correctness, reliability, availability, latency, scalability, maintainability, cost, and safety.
Apply privacy protections and agent-security controls, including prompt-injection defenses, least-privilege tool access, identity and authorization controls, data boundary enforcement, approval gates, auditability, and fail-safe behavior.
Ground agent responses and actions in trusted, current signals by integrating with Azure data sources, APIs, and cloud-native services.
Implement CI/CD and automated testing with feature flags, staged rollouts, tracing, logging, metrics, and alerting.
Diagnose production issues, participate in incident response, and drive corrective improvements.
Evaluate models, frameworks, prompts, retrieval approaches, and architectural options using experiments and production telemetry.
Balance quality with reliability, latency, cost, maintainability, and operational complexity.
Instrument solutions and use results to improve automation quality, case-volume reduction, resolution time, engineering efficiency, and customer satisfaction.
Lead design reviews, resolve technical challenges, coordinate implementation across contributors, and align partner teams around shared interfaces and delivery goals.
Support other engineers through code reviews, technical guidance, reusable documentation, and practical coaching in applied AI and production software engineering.
Partner with product managers, researchers, platform engineers, Azure service teams, and support-domain experts to deliver durable solutions and influence future platform capabilities.

Requirements

Bachelor's Degree in Computer Science or a related technical field AND 4+ years of technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements for this role.
Microsoft Cloud Background Check: required to pass upon hire/transfer and every two years thereafter.

Preferred Qualifications

Experience building or integrating with LLM-based systems, including prompt engineering, RAG, or agent frameworks.
Familiarity with Azure services such as Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB, or comparable cloud-native technologies.
Experience with CI/CD pipelines, automated testing, and production observability.
Experience with Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, or comparable enterprise AI platforms.
Experience designing AI evaluation systems using rubrics, golden datasets, judge models, simulation, adversarial testing, and offline or online evaluation.
Experience with RAG systems, including indexing, retrieval, ranking, grounding, citation, knowledge freshness, and access control.
Experience implementing agent-security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
Experience building reliable distributed services with standards for scalability, resiliency, performance, observability, and cost efficiency.
Experience with infrastructure as code, container orchestration, feature management, and staged production deployment.
Demonstrated ability to lead technical execution, influence design decisions, and mentor engineers without formal people-management authority.
Experience collaborating across product, platform, research, and partner engineering teams on complex co-development initiatives.
Strong business judgment with experience linking engineering investments to customer and operational outcomes.
Background in customer support engineering, supportability, diagnostics, or customer-experience platforms is a plus.