Principal Engineer Future of Engineering AI Solutions

Introduction: A Career at HARMAN Automotive

Were a global multi-disciplinary team thats putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive we give you the keys to fast-track your career.

• Engineer audio systems and integrated technology platforms that augment the driving experience
• Combine ingenuity in-depth research and a spirit of collaboration with design and engineering excellence
• Advance in-vehicle infotainment safety efficiency and enjoyment

About the Role

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 HARMANs 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.

What You Will Do

• 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.

What You Need To Be Successful

• 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.
• Education: BS MS or PhD in Computer Science Artificial Intelligence Data Science Electrical Engineering Software Engineering Mechanical Engineering Mathematics or equivalent professional experience.

What Makes You Eligible

• Ability to work from an office in Novi MI 3 days per week (hybrid)
• Successfully complete a background investigation and drug screen as a condition of employment

What We Offer

• 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

#Hybrid

#LI-AA1

HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard torace religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age status as a protected veteran status as an individual with a disability or other applicable legally protected characteristics.