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About This Role
Location:
Novi \- Michigan, USA \- Cabot Drive
Job Family:
Artificial Intelligence \& Machine Learning
Worker Type Reference:
Regular \- Permanent
Pay Rate Type:
Salary
Career Level:
T4
Job ID:
R\-54874\-2026
### Description \& Requirements
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
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 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.
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
Pay Transparency
$ 125,250 \- $ 183,700
Dependent on the position offered, other forms of compensation are also available, such as bonuses or commission.
Pay is based on a wide range of factors, including, without limitation, skill set, experience, training, location, and business need. While the above range is a reasonable estimate of the wage range for this position, please note the disclosed range estimate has not been adjusted for the applicable geographical differential associated with the location where the position may be filled. Benefits
HARMAN is interested in the health and wellbeing of you and your family and offers a range of benefits designed to support your needs for holistic wellbeing. Benefits and perks may vary depending on the nature of your employment with HARMAN, and may include paid vacation and holidays, paid sick leave, volunteer leave, and paid bonding and care giver leave. Employees may also be eligible to participate in comprehensive medical, dental, and vision plans, fertility support and adoption assistance, Health Savings and Flexible Spending Accounts, retirement savings plan with employer match, short and long term disability coverage, life insurance, and more.
About HARMAN: Where Innovation Unleashes Next\-Level Technology
Ever since the 1920s, we’ve been amplifying the sense of sound. Today, that legacy endures, with integrated technology platforms that make the world smarter, safer, and more connected.
Across automotive, lifestyle, and digital transformation solutions, we create innovative technologies that turn ordinary moments into extraordinary experiences. Our renowned automotive and lifestyle solutions can be found everywhere, from the music we play in our cars and homes to venues that feature today’s most sought\-after performers, while our digital transformation solutions serve humanity by addressing the world’s ever\-evolving needs and demands. Marketing our award\-winning portfolio under 16 iconic brands, such as JBL, Mark Levinson, and Revel, we set ourselves apart by exceeding the highest engineering and design standards for our customers, our partners and each other.
If you’re ready to innovate and do work that makes a lasting impact, join our talent community today!
Important Notice: Recruitment Scams
Please be aware that HARMAN recruiters will always communicate with you from an '@harman.com', ‘@careers.harman.com’ or ‘@harmanglobal.avature.net’ email address. We will never ask for payments, banking, credit card, personal financial information or access to your LinkedIn/email account during the screening, interview, or recruitment process. If you are asked for such information or receive communication from an email address not ending in one of the above email domains about a job with HARMAN, please cease communication immediately and report the incident to us through: [email protected]. You Belong Here
HARMAN is committed to making every employee feel welcomed, valued, and empowered. No matter what role you play, we encourage you to share your ideas, voice your distinct perspective, and bring your whole self with you – all within a support\-minded culture that celebrates what makes each of us unique. We also recognize that learning is a lifelong pursuit and want you to flourish. We proudly offer added opportunities for training, development, and continuing education, further empowering you to live the career you want.
HARMAN is proud to be an Equal Opportunity employer. HARMAN strives to hire the best qualified candidates and is committed to building a workforce representative of the diverse marketplaces and communities of our global colleagues and customers. All qualified applicants will receive consideration for employment without regard to race, 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. HARMAN attracts, hires, and develops employees based on merit, qualifications and job\-related performance.(www.harman.com)
HARMAN is committed to providing reasonable accommodations to applicants with disabilities. If you need assistance or an accommodation during the application process, please contact us at [email protected]. Requests will be considered on a case\-by\-case basis in accordance with applicable law.
Salary Context
This $125K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Harman, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($154K) sits 28% below the category median. Disclosed range: $125K to $183K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Harman AI Hiring
Harman has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Novi, MI, US. Compensation range: $183K - $183K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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