Principal Engineer - Future of Engineering AI Solutions

Introduction: A Career at HARMAN Automotive

We’re a global, multi-disciplinary team that’s 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

Drive the architecture and delivery of scalable AI and Generative AI capabilities that transform HARMAN Automotive R&D processes, engineering tool chains, 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 tool chains, 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.

• Define and build the scalable AI solution architecture and roadmap for IT/Digital enablement of R&D, focused on connected tool chains, 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, Open Claw or similar open-source agent platforms, n8n, Out Systems AI, Lang Chain, Lang Graph, Llama Index, Semantic Kernel, Auto Gen, Graph RAG, CrewAI, MCP, A2A, and Lang Fuse 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…