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Generative Ai Platform Engineer Jobs

5 jobs found

Today

AI Engineer

Hollstadt Consulting Minneapolis, MN 🇺🇸 Remote

Minneapolis, MN

Today

$105k - $140k/yr Full time AI Ai Engineer Ai Engineering

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Job Description

Hollstadt Consulting is building out its AI Center of Excellence and needs an AI/ML Engineer to support cross-functional teams with production-focused machine learning, AI, Generative AI, and agentic AI solutions. This role is centered on designing and delivering end-to-end capabilities, from pipeline development to testing and secure integration across enterprise environments. This position is remote in Minnesota, with local preference.

Key work includes building production AI/ML pipelines and GenAI or agent capabilities tailored to domain-specific needs. The role supports automation of complex document understanding and structured content extraction, while also establishing reliability measures such as LLM testing frameworks and regression suites for drift and prompt breakage.

Responsibilities

Collaborate across GenAI platforms and libraries, including AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms.
Automate workflows to extract highly accurate structured outputs from complex, multimodal unstructured content using tools such as AWS Textract and Bedrock.
Design and build Model Context Protocol (MCP) hosts, clients, and servers.
Establish and apply frameworks for automated LLM testing.
Create regression test suites to detect drift and prompt breakage.
Integrate with internal and external web services using secure authentication and authorization mechanisms.
Adopt safe practices to protect against prompt injections and jailbreaks, aligning with enterprise security guidelines.
Design, develop, and deploy production-grade traditional ML models including regression, classification, clustering, and recommender systems.
Design, maintain, and optimize end-to-end AI/ML pipelines across data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure such as AWS (or equivalent).
Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective in cloud environments.
Create reusable components, frameworks, and best practices to accelerate AI development.
Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval-Augmented Generation (RAG), and custom pipelines.
Design and develop interoperable AI agents using MCP and/or Google A2A.
Partner with data scientists, architects, product managers, business stakeholders, and technical teams to align AI solutions with organizational goals.
Provide hands-on technical support and mentorship to technical teams across the enterprise.

Requirements

3+ years of experience designing and deploying ML/AI solutions in real-world environments.
Very strong Python skills.
Strong hands-on experience with LLM APIs (including OpenAI, Azure OpenAI, Gemini, Anthropic) using Python and Python-based frameworks.
Strong hands-on experience with prompt engineering, context construction, and grounding strategies.
Strong hands-on experience with RAG workflows, including extracting, chunking, and creating embeddings from unstructured documents from sources such as O365 (email, Word, Excel), PDFs, and webpages.
Comfort building MCP clients, servers, and hosts.
Expertise building REST APIs and integrating with internal and external APIs.
Hands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents.
Knowledge of Google A2A.
Deep experience in AWS including Lambda, Bedrock, Step Functions, API Gateway, and IAM.
Strong experience with observability tools such as Dynatrace or similar GenAI observability tools.
Excellent GenAI foundations and concepts, including enterprise data privacy, AI governance, and observability.
Proficiency in Python and common ML/AI libraries such as TensorFlow, PyTorch, and scikit-learn.
Strong understanding of data engineering, SQL, and feature engineering.
Hands-on experience with cloud services including AWS SageMaker, Lambda, ECS, S3, and IAM.
Familiarity with containerization (Docker) and orchestration (e.g., Airflow, Kubeflow).
Working knowledge of version control and collaboration tools such as Git, Jira, and Confluence.
Bachelor’s degree in computer science, engineering, or a related field.
Knowledge of machine learning algorithms, deep learning frameworks, cloud AI technologies, GenAI technologies, and emerging agentic AI technologies.
Knowledge of cloud platforms (AWS, Azure, GCP) for scalable AI/ML development.
Knowledge of responsible AI principles including bias mitigation and ethical deployment.
Knowledge of ML Ops best practices including CI/CD for ML, model monitoring, and versioning.
Ability to build robust, scalable, and efficient AI/ML solutions in cloud-native environments.
Ability to translate ambiguous business problems into clear technical ML/AI tasks.
Ability to communicate complex ideas clearly to technical and non-technical stakeholders.
Ability to learn and adapt quickly to emerging AI technologies, techniques, and tools.

Technologies

AWS, SalesForce, Oracle, Snowflake, MS Copilot
AWS Textract, Bedrock, Model Context Protocol (MCP), Google A2A
OpenAI, Azure OpenAI, Gemini, Anthropic
REST APIs, Dynatrace
TensorFlow, PyTorch, scikit-learn
O365, PDFs, webpages
Intelligent Document Processing, OCR
AWS Lambda, AWS Step Functions, AWS API Gateway, AWS IAM, AWS Sagemaker, AWS ECS, AWS S3
Docker, Airflow, Kubeflow
Git, Jira, Confluence, CI/CD, ML Ops
Retrieval-Augmented Generation (RAG), prompt engineering, Context Engineering, grounding strategies, embeddings

Compensation & Logistics

Location: Minnesota (remote); local preferred but not required
Salary: USD 105,000 - 140,000 per year, plus 8% AIP eligibility
Start date: 9/28/2026

Benefits

Comprehensive Benefit Plan including medical, dental, vision, life insurance, short-term disability, long-term disability, paid sick leave, and retirement benefits
401(k) + Matching: match on the first 4% of contributions
Bonus opportunities including Longevity Award bonus and referral bonus
Professional development with on-demand training through a consultant portal, including upskilling in Artificial Intelligence (AI)
Ongoing support and networking through a Consultant Coach program

Preferred Qualifications

Master’s in a related technical field
Hands-on experience with agentic AI frameworks
Prior contributions to open-source AI/ML projects or published research
AI/ML certifications from cloud providers
Experience in highly regulated industries (e.g., healthcare, finance) a plus

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Today

AI Engineer

LawPro.ai Virginia

Virginia

Today

Full time Ai Engineering Artificial Intelligence Engineering

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Job Description

LawPro.ai is building AI-powered data insights and analytics, and they’re hiring an AI Engineer in Virginia (onsite) to own the evaluation, selection, and continuous optimization of large language models and AI processes. This role blends AI research with production engineering, with end-to-end responsibility for model transitions, pipeline optimizations, monitoring, and documentation.

What you’ll deliver

Run a systematic, ongoing process to evaluate new and emerging LLMs on accuracy, relevancy, speed, and cost, continuously benchmarking against the specific tasks in the orchestration pipeline.
Design and operate rigorous Evals and build an internal EvalOps culture to measure output accuracy, relevance, faithfulness, and speed, with focused effort on reducing hallucinations in medical record summarization and legal document analysis.
Track provider changes across the LLM landscape to identify deprecation timelines, then execute full transitions by integrating replacement models into the production pipeline and updating for model behavior differences.
Improve LLM-based orchestration for document understanding, medical record summarization, case chronology generation, and drafting support by implementing code changes, deployments, and production validation from start to finish with a bias for surgical execution.
Communicate evaluation findings with product and GTM stakeholders, then lead the technical implementation directly to ensure frictionless handoffs between discovery, staging, and live production deployments.
Ship model changes into production by writing integration code, managing deployments, running validation tests, and ensuring clean rollouts.
Implement monitoring and observability for model performance, including benchmarking outputs and cost, detecting drift, and reporting continuously to management using micro-benchmarking to track token-level latency, output drift, and cost efficiency across pipeline components.
Maintain thorough documentation covering evaluation methodologies, model comparison results, transition decisions, and runbooks for the systems you own.

What you bring

5+ years of AI/ML engineering experience evaluating, fine-tuning, and deploying large language models in production, including building and deploying models to AWS or GCP infrastructure at scale.
Hands-on development and implementation of multiple RAG solutions.
Hands-on experience leveraging embedding models and vector databases.
Hands-on experience building agentic workflows and implementing practical EvalOps or Evals-as-a-Service architecture.
Deep familiarity with the LLM ecosystem, with ability to assess model capabilities, limitations, and fit using tradeoffs across routing, cost, quality, speed, and capability.
Proven experience designing evaluation frameworks for output quality measurement, including hallucination detection in high-stakes domains (legal, medical, or similar).
Strong software engineering foundation with production experience building LLM orchestration frameworks and multi-model pipelines.
Comfort working in a fast-paced, high-ambiguity environment with strong ownership, tight feedback loops, and a bias toward systematic process-building.
Excellent communication skills to translate complex evaluation findings into clear recommendations for engineering, product, and non-technical stakeholders.
Bonus: experience with unstructured medical or legal document processing, or background in classical ML (statistics, embeddings, retrieval-augmented generation).

Technology focus

AWS, GCP

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Today

Robotics AI Engineer

Apple Cupertino, CA

Cupertino, CA

Today

$185k - $325k/yr Full time Python Agentic Ai Ai Automation

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Job Description

Apple is hiring a Robotics AI Engineer in Cupertino, CA (onsite) to turn natural user intent into adaptive, real-time behaviors.

Responsibilities

Apply frontier reasoning models to convert user intent into robust closed-loop plans that clarify, adapt, and replan as context changes
Design agent harnesses that ground model reasoning in the user’s context and a structured set of system capabilities
Use multimodal models to interpret intent from audio, text, and vision inputs
Evaluate models by mapping product requirements to appropriate metrics, then use results to identify failures and drive improvements
Fine-tune and distill frontier models into smaller, efficient models for on-device deployment
Deploy and optimize models on Apple silicon for real-time, efficient, interactive performance
Rapidly prototype and assess new AI models, tools, and agent harnesses to advance product capabilities

Requirements

PhD in Computer Science, Machine Learning, or a related field
Experience with model fine-tuning and distillation for efficient on-device deployment
Hands-on experience with inference engines: llama.cpp, vLLM, MLX, or Core ML
Experience building agent harnesses for multi-step, closed-loop reasoning
Track record of shipping products to users
Proficiency in Python and ML frameworks: PyTorch or TensorFlow
BS or MS in Computer Science or Machine Learning
Strong foundation in and deep understanding of modern machine learning methods
Experience training or fine-tuning your own transformer and/or diffusion models
Proficiency using coding agents: Claude Code and Codex
Genuine excitement for experimenting with the latest AI models, tools, and multimodal capabilities

Preferred qualifications

PhD in Computer Science, Machine Learning, or a related field
Experience with model fine-tuning and distillation for efficient on-device deployment
Hands-on experience with inference engines such as llama.cpp, vLLM, MLX, or Core ML
Experience building agent harnesses for multi-step, closed-loop reasoning
Track record of shipping products to users
Proficiency in Python and ML frameworks (PyTorch or TensorFlow)

Technologies

Python
PyTorch
TensorFlow
llama.cpp
vLLM
MLX
Core ML
Claude Code
Codex
Transformer models
Diffusion models
Apple silicon

Education

BS or MS in Computer Science or Machine Learning

Pay & Benefits

Base pay range: $184,700 - $324,800 per year
Apple base pay is determined within a range based on skills, qualifications, experience, and location
Potential for discretionary bonuses or commission, as well as relocation (eligibility may apply)
Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary restricted stock unit awards (eligibility may apply)
Opportunity to become an Apple shareholder through discretionary employee stock programs
Option to purchase Apple stock at a discount via the Employee Stock Purchase Plan (voluntary participation)
Note: Benefits, compensation, and employee stock programs are subject to eligibility requirements and plan terms

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Today

Google Cloud AI Engineer

Altimetrik Corp Remote 🌍 Remote

Remote

Today

Full time Artificial Intelligence Big Data Bigquery

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Job Description

Altimetrik Corp is seeking a Google Cloud AI Engineer to design and deploy AI agents grounded in unique business data as part of Google’s Data Cloud for the agentic era. This role blends agentic design with AI-on-data strategy, with a strong focus on real-time inference, secure data grounding, and turning high-level business goals into robust technical architectures.

What you’ll be doing

Develop intelligent agents using Vertex AI Agent Builder to automate complex business workflows.
Use the Agent Developer Kit (ADK) to build and manage multi-agent systems that collaborate to address end-to-end business challenges.
Integrate tools such as MCP (Model Context Protocol) Toolbox to securely connect agents to enterprise databases including BigQuery and Spanner.
Use Vertex AI for model training, tuning, and deployment, ensuring smooth integration with BigQuery for feature engineering.
Build and optimize streaming data pipelines (for example, via Dataflow) to support real-time inference using RunInference API or Vertex AI endpoints.
Ground models in live business context by leveraging vector engines within BigQuery or AlloyDB to reduce “AI amnesia”.
Attend internal and client-facing meetings promptly, and deliver regular, structured status updates on milestones and technical blockers.
Request help when facing technical hurdles and contribute to a collaborative troubleshooting environment.

What you bring

Proven experience with Model Garden, Vertex AI Pipelines, and model evaluation.
Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques including scaling, encoding, and imputation.
Hands-on experience with Google Cloud Storage and Vertex AI endpoints.
Familiarity with stateful real-time processing and recent innovations in agentic architectures.

Helpful background

Experience in financial services or retail to support industry-specific data logic (for example, credit risk, royalty forecasting, or search relevance).
Knowledge of privacy and compliance practices for handling PII using masking and redaction.

Benefits

401(k)
Health insurance

Location and travel

Location: Remote

Travel: 25% (Required)

Application check

Willingness to complete a pre-employment background check contingent upon an offer.

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2d ago

Senior Principal AI Engineer

Vertex Pharmaceuticals Boston, MA

Boston, MA

2d ago

$188k - $282k/yr Full time Senior Agent Platform Ai Agent Platform

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Job Description

Vertex Pharmaceuticals is hiring a Senior Principal AI Engineer in Boston to design and scale shared agentic AI platform capabilities for production model integration and lifecycle management.

Responsibilities

Build/bring-your-own-agents capability (primary focus): create frameworks, SDKs, templates, interfaces, and guardrails so teams across Vertex can build, integrate, customize, and operationalize agents using shared platform standards
Platform services: deliver model integration, prompt and workflow orchestration, tool use, memory patterns, and agentic task coordination for other teams to build upon
Agent lifecycle and quality: implement agent registration, configuration, testing, deployment, versioning, monitoring, and retirement, plus evaluation and benchmarking frameworks
Developer experience ownership: enable self-service onboarding, documentation, reference implementations, and enablement resources to reduce time from concept to production
Standards and technical leadership: define platform APIs, service contracts, architecture patterns, and engineering practices that keep custom agents safe, reliable, and supportable
Architect and develop shared AI and agentic platform services for enterprise AI products and internal workflows
Own build/bring-your-own-agents systems that let teams create, register, integrate, deploy, and manage their own agents within the enterprise platform
Establish reusable SDKs, templates, interfaces, and guardrails to standardize agent build and onboarding
Define and deliver agent lifecycle capabilities including registration, configuration, testing, deployment, monitoring, versioning, and retirement
Build and maintain integrations with foundation models, model gateways, APIs, enterprise tools, and related AI infrastructure
Design and implement systems for prompt orchestration, workflow execution, tool use, memory patterns, and agentic task coordination
Develop reusable frameworks for evaluation, benchmarking, and validation of AI model, agent, and workflow performance
Establish observability capabilities including monitoring, tracing, logging, and alerting for AI workloads and autonomous agent interactions
Optimize performance, scalability, latency, reliability, and cost efficiency for production AI and agentic systems
Partner with product, data, engineering, security, and architecture teams to enable enterprise-ready AI solutions
Translate prototypes and experimental concepts into hardened, maintainable, production-grade services
Define engineering standards, best practices, and design patterns for AI platform development and deployment
Support governance, risk management, and responsible AI practices via measurable controls, policy enforcement, and technical safeguards for agent behavior
Drive platform adoption through reusable components, documentation, onboarding patterns, and developer enablement resources
Mentor engineers and provide technical leadership across AI platform initiatives
Evaluate emerging tools, frameworks, and architectural patterns in generative AI and agentic systems to inform platform strategy

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Machine Learning, Data Engineering, or a related technical field; advanced degree preferred
Significant industry experience in software engineering, machine learning engineering, or AI platform development, including experience in senior or principal-level technical roles
Proven track record designing and delivering production-scale AI or ML platforms
Strong experience building distributed systems, APIs, microservices, and cloud-native applications
Demonstrated experience operationalizing machine learning, generative AI, or agent-based solutions in enterprise environments
Experience designing extensible platform capabilities that enable internal teams to build or integrate custom applications, tools, or services
Deep understanding of software engineering best practices including testing, CI/CD, version control, code review, and system reliability
Deep understanding of AI-native software engineering practices, including establishing standards, governance, and best practices for responsible use of AI coding assistants and software engineering agents
Experience defining architecture, standards, and reusable services for large-scale enterprise environments
Experience leading complex technical initiatives and influencing architecture across cross-functional teams
Strong communication skills with the ability to explain complex technical concepts to varied audiences
Experience balancing experimentation speed with production engineering discipline, security, and maintainability

Technologies

APIs
Microservices
Cloud-native applications
CI/CD
Version control
AI/ML platforms
Foundation models
Model gateways
CLIs
Vector databases

Benefits

Eligible for an annual bonus and annual equity awards
Health insurance
Dental benefits
Vision benefits
Generous paid time off, including a week-long company shutdown in the Summer and the Winter
Educational assistance programs, including student loan repayment
Generous commuting subsidy
Matching charitable donations
401(k)
Overtime pay (in accordance with federal and state requirements, for some roles)

Preferred Skills

Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline
Experience with enterprise AI platforms, developer platforms, or internal tooling ecosystems
Experience building frameworks or platforms that support bring-your-own-component or extensible developer patterns
Developer experience focus, including self-service onboarding, SDKs, CLIs, sandboxes, templates, and documentation to reduce friction and accelerate time to first deployment
Familiarity with model gateways, retrieval-augmented generation, and evaluation frameworks
Experience implementing AI governance, responsible AI controls, and compliance-oriented technical solutions
Knowledge of vector databases, knowledge retrieval systems, and orchestration layers for intelligent applications
Experience in regulated industries such as biotechnology, pharmaceuticals, healthcare, or life sciences
Strong mentoring and technical leadership experience in highly collaborative environments
Ability to assess emerging AI technologies and translate them into practical platform capabilities

Pay Range

$188,000 - $282,000 per year

Location

Boston, MA (onsite)

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