Principal AI Engineer – Generative AI & Agentic AI

About the role

We are working with an award-winning global manufacturing and technology company that is making a significant investment in AI as part of a major digital transformation.

They are looking for a Principal AI Engineer to play a key role in defining and building how Generative AI and Agentic AI are used across a large, complex engineering and R&D organization.

This is a highly visible position for someone who combines strong hands-on AI engineering experience with an architectural mindset. You’ll have significant influence over technology decisions and will help take AI initiatives from concept and architecture through production implementation.

The work will include Agentic AI, RAG, LLM orchestration, engineering copilots, intelligent automation, semantic search, conversational AI and knowledge discovery, along with the architecture required to support these capabilities at enterprise scale.

What People are saying about this company:

"Great place to work! Work culture is fantastic! Top notch leadership team, Innovative and cutting-edge environment "

"Benefits are excellent"

"Your growth is in your hands"

Title: Principal AI Engineer – Generative AI & Agentic AI

Location: Detroit, MI or Stamford, CT - Hybrid

Salary: $145,000 - $185,000 +Bonus

Responsibilities:

This is a highly visible Principal-level, hands-on AI architecture and engineering role with the opportunity to help define how AI is used across complex R&D processes, engineering toolchains, and digital workflows. You will work at the intersection of AI, enterprise architecture, data, software engineering, and R&D, helping take AI initiatives from architecture and experimentation through scalable production implementation.

As Principal AI Engineer, you will help define and build the organization's enterprise AI foundation, including Agentic AI, RAG, LLM orchestration, AI agents, context and memory services, engineering copilots, intelligent automation, observability, guardrails, and secure enterprise AI architecture.

This is an excellent opportunity for a senior AI engineer or architect who wants to remain highly technical while having significant influence over AI architecture, technology selection, engineering standards, and the future direction of enterprise AI.

Define and build the scalable AI and Generative AI architecture and roadmap supporting R&D, engineering productivity, connected toolchains, automation, analytics, and integrated engineering data.

Architect AI capabilities across the R&D lifecycle, including requirements and specification analysis, architecture support, engineering project workflows, test management, quality, compliance, traceability, ASPICE, and functional safety.

Design reusable AI solutions for engineering copilots, intelligent automation, conversational AI, knowledge discovery, document intelligence, semantic search, recommendations, large-scale log analysis, simulation assistance, generative design, and engineering analytics.

Build end-to-end Agentic AI architectures, including agent registries, identity, catalogs, context and memory management, orchestration, tool/function calling, human-in-the-loop workflows, observability, guardrails, and secure enterprise integrations.

Design and implement advanced RAG and knowledge retrieval solutions across complex engineering data, including requirements, specifications, architecture artifacts, test cases, defects, quality records, compliance documentation, standards, and other technical knowledge.

Establish scalable LLM architecture and orchestration, including model routing, prompt/version management, caching, token optimization, evaluation, fallback strategies, latency and throughput optimization, and cost controls.

Evaluate and industrialize the modern AI engineering toolchain, including coding agents, AI development frameworks, workflow automation, conversational AI platforms, model gateways, evaluation frameworks, and observability platforms.

Serve as a technical leader and trusted AI expert, mentoring engineers and architects while establishing practical patterns for building secure, scalable, responsible, and production-ready AI solutions.

Requirements:

10+ years of experience across software engineering, AI/ML engineering, data engineering, enterprise architecture, or digital transformation, with recent hands-on experience designing and delivering production-grade AI and Generative AI solutions within the automotive industry.

Strong understanding of R&D and complex engineering environments, ideally involving automotive, embedded systems, electronics, software engineering, mechanical engineering, or sophisticated product development.

Experience integrating AI into engineering and R&D toolchains such as requirements management, ALM/PLM, architecture management, project/task management, testing, quality, defect management, compliance, and traceability.

Deep hands-on experience with Generative AI, LLMs, RAG, semantic search, embeddings, vector databases, prompt engineering, model orchestration, Agentic AI, conversational AI, and enterprise AI integration.

Demonstrated ability to architect complete Agentic AI solutions, including agents, context, memory, orchestration, tool integration, human approvals, observability, guardrails, security, and enterprise system integration.

Strong understanding of modern AI engineering tools and frameworks, including AI coding assistants, agent frameworks, workflow automation platforms, model gateways, evaluation frameworks, and AI observability tools.

Experience with technologies such as Claude Code or similar coding agents, GitHub Copilot, Cursor, OpenClaw or similar agent platforms, n8n, OutSystems AI, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, MCP, A2A, LangFuse, or comparable technologies is highly desirable.

Ability to evaluate emerging AI technologies for enterprise use, considering security, privacy, governance, scalability, integration, observability, licensing, deployment options, cost, and long-term maintainability.

Strong understanding of key LLM architecture decisions, including RAG vs. long-context approaches, fine-tuning vs. prompt engineering, open-source vs. commercial models, model selection/routing, cost vs. latency, and accuracy vs. explainability.

Experience working with leading foundation model platforms and providers such as AWS Bedrock, Azure OpenAI, OpenAI, Anthropic, Meta/Llama, Mistral, or comparable platforms.

Strong programming skills in Python and modern API-driven application development, including experience with FastAPI, REST APIs, GraphQL, event-driven architectures, and/or microservices.