Principal AI Engineer (m/f/d)

As Principal AI Engineer in Harman Automotive AI Lab, you will lead the design, deployment, and optimization of AI-driven systems that boost engineering productivity and operational efficiency. You’ll collaborate across business, engineering, and IT to translate needs into scalable AI solutions that affect real-world performance. You will drive experimentation, governance, and continuous improvement while mentoring junior engineers. This role sits at the intersection of AI, software-defined vehicles, and cross-functional collaboration to transform automotive engineering.

• hybrid work environment
• office onsite three days a week
• employee discounts on HARMAN and Samsung products
• HARMAN University learning and development programs

• Define problem statements with cross-functional teams and translate them into technical concepts
• Lead development and deployment of AI/ML projects aligned with business KPIs
• Steer experimentation, validation, and optimization for robust, scalable solutions
• Integrate feedback loops and performance tuning into deployed AI systems
• Champion ML best practices: reproducibility, version control, model governance, and ethical AI
• Stay current with AI/ML advancements and apply them to engineering and operations challenges
• Mentor junior team members and foster a culture of innovation and excellence

• 7+ years of hands-on experience in designing and deploying production-grade ML/AI solutions with end-to-end integration
• Proven experience delivering Generative AI solutions at scale including deployment and evaluation in real-world settings
• Deep understanding of Large Language Model architectures, prompting strategies, and inference optimization
• 4+ years of experience with AI/ML workloads on AWS or Azure and knowledge of key services for compute, storage, orchestration, and model deployment
• Proficiency in Python with strong software engineering skills (OOP, TDD, clean code)
• Solid understanding of ML system design (data pipelines, model lifecycle management, performance monitoring, MLOps)
• Ability to lead technical discussions across data scientists, engineers, product managers, and operations teams

• collaboration
• communication
• mentoring and leadership
• Generative AI
• LLM architectures
• prompting strategies