Principal Engineer - Future of Engineering AI Solutions

Harman Becker Automotive Systems Manufacturing Kft • Novi, MI

12d • Hybrid

About The Position

Drive the architecture and delivery of scalable AI and Generative AI capabilities that transform HARMAN Automotive R&D processes, engineering toolchains, and digital workflows. This role sits at the intersection of IT/Digital, R&D, enterprise architecture, data, and engineering platforms. You will build the AI solution landscape from a process and tooling standpoint, enabling connected toolchains, integrated engineering data, automation, analytics, and intelligent experiences across R&D. The primary focus is HARMAN's embedded engineering landscape across Mechanical, Electronics, and Software domains, including RFI/SPEC management, requirements management and engineering, architecture, project and task management, test management, quality, ASPICE, Functional Safety (FuSa), compliance, traceability, and connectivity to the appropriate AME technology ecosystem. The Software engineering toolchain is a highly dynamic area with significant opportunity for AI-assisted development, engineering automation, large-scale log analysis, simulation support, test generation, and knowledge discovery. Additional focus areas include generative design for hardware and mechanical engineering, conversational AI embedded into engineering applications, AI-assisted simulation, and analytics over complex engineering data. As Principal Engineer - AI, you will define and deliver enterprise-grade AI foundations including agentic AI architecture, RAG, LLM orchestration, AI toolchain enablement, agent development patterns, context and memory services, observability, guardrails, data security, and cost-effective high-performance LLM architecture. You will also mentor engineers and architects on practical, responsible, and effective use of AI techniques, tools, and patterns.

Requirements

• 10+ years of experience in software engineering, data engineering, AI/ML engineering, enterprise architecture, or digital transformation, with hands-on experience delivering production-grade AI or Generative AI solutions in the automotive industry.
• Strong understanding of R&D and engineering processes, preferably in embedded systems, automotive, electronics, software, mechanical engineering, or complex product development environments.
• Experience with engineering toolchains such as RFI/SPEC management, requirements management, ALM/PLM, architecture management, project and task management, test management, quality management, defect management, compliance workflows, and traceability.
• Hands-on experience with Generative AI, LLMs, RAG, semantic search, embeddings, vector databases, prompt engineering, model orchestration, agentic AI frameworks, conversational AI, and enterprise AI integration patterns.
• Ability to design end-to-end agentic AI architecture, including agent registry, identity, catalog, context, memory, orchestration, tool integration, human approvals, observability, guardrails, and secure execution.
• Practical proficiency with modern AI engineering toolchains, including AI-assisted coding tools, agent development frameworks, workflow automation platforms, low-code AI platforms, conversational AI builders, model gateways, evaluation frameworks, and observability tools.
• Familiarity with tools and ecosystems such as Claude Code or equivalent coding agents, GitHub Copilot, Cursor, OpenClaw or similar agent platforms, n8n, OutSystems AI, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, MCP, A2A, LangFuse, and related technologies is highly desirable.
• Ability to evaluate new AI tools for enterprise readiness, including security, data privacy, extensibility, integration fit, observability, cost, governance, licensing, deployment model, and long-term maintainability.
• Strong knowledge of LLM architecture trade-offs, including RAG versus long-context models, fine-tuning versus prompt engineering, open-source versus commercial models, cost versus latency, and accuracy versus explainability.
• Experience with model providers and foundation platforms such as AWS Bedrock, Azure OpenAI, OpenAI, Anthropic, Meta/Llama, Mistral, or similar ecosystems.
• Strong programming skills in Python and modern API-based application development; experience with frameworks such as FastAPI and integration with REST, GraphQL, event-driven, or microservice-based architectures.
• Experience with vector databases and search platforms such as Pinecone, Weaviate, FAISS, Milvus, pgvector, Elasticsearch, OpenSearch, or equivalent technologies.
• Experience with cloud, container, and DevOps technologies such as AWS, Azure, GCP, Docker, Kubernetes, Terraform, CI/CD, observability platforms, and secure enterprise deployment patterns.
• Understanding of data architecture, data pipelines, data governance, access control, and engineering data integration across structured, semi-structured, and unstructured sources.
• Familiarity with automotive engineering standards and compliance areas such as ASPICE, Functional Safety (FuSa), quality management, validation, traceability, and engineering governance is highly desirable.
• Ability to influence and mentor engineers, architects, product owners, and stakeholders on responsible AI adoption, scalable solution design, and practical use of AI-assisted development.
• BS, MS, or PhD in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, Software Engineering, Mechanical Engineering, Mathematics, or equivalent professional experience.

Responsibilities

• Define and build the scalable AI solution architecture and roadmap for IT/Digital enablement of R&D, focused on connected toolchains, integrated data, automation, analytics, and engineering productivity.
• Architect AI capabilities across the R&D lifecycle, including RFI/SPEC analysis, requirements engineering, architecture support, project and task management, test management, quality workflows, ASPICE, FuSa, compliance evidence, and traceability.
• Design reusable AI solution patterns for engineering automation, conversational AI, knowledge discovery, document intelligence, intelligent recommendations, large-scale log analysis, simulation assistance, generative design exploration, and engineering analytics.
• Develop full agentic AI architectures including agent registry, agent identity, agent catalog, context and memory management, orchestration, tool and function calling, human-in-the-loop workflows, observability, guardrails, and secure enterprise integration.
• Design and implement RAG solutions over heterogeneous engineering datasets such as requirements, specifications, architecture artifacts, test cases, defect data, quality records, compliance artifacts, lessons learned, standards, and unstructured technical documentation.
• Establish LLM foundation architecture with model routing, prompt and version management, token optimization, caching, evaluation, fallback strategies, latency and throughput tuning, and cost-control mechanisms.
• Evaluate, standardize, and industrialize the AI engineering toolchain, including coding agents, agent development platforms, workflow automation tools, low-code AI platforms, conversational builders, model gateways, evaluation tools, and observability platforms.
• Partner with R&D tool owners and platform teams to integrate AI with requirements management, ALM/PLM, architecture management, test management, quality systems, data platforms, cloud services, and AME technology ecosystems.
• Embed AI into custom enterprise applications through agent frameworks, conversational interfaces, APIs, reusable AI services, and workflow automation patterns.
• Apply and guide usage of tools and ecosystems such as Claude / Codex Ai assisted development/Github Copilot, OpenClaw or similar open-source agent platforms, n8n, OutSystems AI, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, Graph RAG, CrewAI, MCP, A2A, and LangFuse where appropriate for enterprise R&D use cases.
• Establish practical guidelines for AI-assisted development and vibe coding that preserve engineering discipline, including architecture reviews, code quality, security scanning, test automation, documentation, traceability, and compliance alignment
• Establish AI governance, data security, access control, model and data lineage, responsible AI practices, evaluation standards, observability, and guardrails for enterprise engineering environments.
• Mentor the engineering community on effective use of RAG, agents, prompt engineering, fine-tuning trade-offs, semantic search, workflow automation, conversational AI, token optimization, and AI toolchain adoption.

Benefits

• Access to employee discounts on world-class products (JBL, HARMAN Kardon, AKG, and more)
• Extensive training opportunities through our own HARMAN University
• Competitive wellness benefits
• Tuition reimbursement
• “Be Brilliant” employee recognition and rewards program
• An inclusive and diverse work environment that fosters and encourages professional and personal development

What This Job Offers

• Job Type: Full-time
• Career Level: Principal