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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\-54875\-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 hands\-on delivery of AI and Generative AI solutions that streamline supply chain workflows and deliver measurable business value through hours saved, cycle\-time reduction, improved decision quality, risk mitigation, and the breadth of users served. You will architect, develop, and maintain production\-grade systems encompassing RAG pipelines, agentic tools, model routing, vector search, evaluation and guardrails, and observability, all tightly integrated with internal platforms, enterprise datasets, and supply chain systems. This is primarily a hands\-on GenAI and software engineering role, with supply chain expertise providing the domain context for solution design and delivery.
What You Will Do
- Automate high\-impact supply chain workflows for internal stakeholders, prioritizing initiatives with the greatest time savings, business impact, and user reach.
- Deliver production\-ready copilots and applications for knowledge search, document summarization, intelligent recommendations, conversational analytics, exception management, and end\-to\-end workflow automation.
- Apply GenAI and software engineering to supply chain use cases across procurement; supplier collaboration and management; risk management; quality; costing; engineering; materials and warehouse management; finished\-goods and component\-level planning; and ESG.
- Architect and develop scalable, high\-performance data and AI systems that support RAG, agentic workflows, secure tool use, and model orchestration.
- Own the complete solution lifecycle, from problem definition and rapid prototyping through rigorous evaluation, production deployment, ongoing monitoring, and continuous improvement.
- Design and implement RAG pipelines over heterogeneous and often messy enterprise and supply chain data, including contracts, purchase orders, supplier documents, bills of material, requirements, quality records, audit artifacts, planning data, business rules, and unstructured content. Select embedding strategies, chunking approaches, vector search configurations, rerankers, metadata or knowledge\-graph enrichment techniques, and routing policies to maximize retrieval quality.
- Develop agentic workflows leveraging LangChain, LlamaIndex, Model Context Protocol (MCP), and agent\-to\-agent (A2A) protocols; build secure tools that allow agents to retrieve data and execute approved actions in enterprise systems.
- Integrate AI solutions with enterprise applications and data platforms through APIs, events, batch pipelines, and governed access patterns; design integrations that are resilient, observable, and maintainable.
- Evaluate when to use platform\-native embedded AI capabilities versus custom\-built GenAI components, and design modular solutions that can evolve with the enterprise tool landscape.
- Translate subject\-matter\-expert knowledge into robust prompts, tools, workflow logic, and validation rules; evaluate trade\-offs among prompt engineering, retrieval augmentation, fine\-tuning, and deterministic software.
- Work hands\-on with large language models, vector databases such as Pinecone and FAISS, and agent memory systems.
- Establish operational excellence through rigorous SLAs; safety and guardrail mechanisms; prompt and version management; transparent evaluation; latency and throughput optimization; cost controls; load balancing; fallback or model\-routing strategies; and human review for process\-critical decisions.
- Establish observability using tools such as Datadog, Grafana, and LangFuse, along with model and data governance, access controls, auditability, and operational support appropriate for internal enterprise environments.
- Build and maintain data products, lakes, and warehouses using platforms such as Snowflake, Delta Lake, BigQuery, and Microsoft Fabric to support supply chain AI use cases.
- Build internal copilots and customer\-facing features using React, Node.js, and Python with REST or GraphQL backends; containerize applications with Docker, orchestrate with Kubernetes, automate CI/CD pipelines, and manage infrastructure as code using tools such as Terraform.
- Collaborate closely with supply chain subject\-matter experts, requirements, testing, validation, cybersecurity, data, platform, and enterprise application teams; communicate proactively and iterate rapidly in a fast\-paced environment.
What You Need To Be Successful
- 8\+ years of experience building production software, ideally including ML systems and hands\-on work with LLMs and Generative AI; demonstrated technical leadership while remaining deeply hands\-on.
- Programming: Python (FastAPI, NumPy, Pandas, scikit\-learn, Pydantic, Jinja2\) and Node.js; strong proficiency with APIs and distributed systems.
- LLMs and Frameworks: Hands\-on experience with at least one major deep learning or LLM stack, such as PyTorch/Transformers or TensorFlow/Keras, and orchestration frameworks such as LangChain or LlamaIndex.
- Model Providers: Working familiarity connecting to inference providers and model ecosystems such as AWS Bedrock, OpenAI, Anthropic, Meta/Llama, and Mistral.
- Data and Storage: SQL and NoSQL databases (PostgreSQL, DynamoDB), Elasticsearch for search and analytics, and vector databases (Pinecone, Weaviate, FAISS, Milvus, pgvector).
- Cloud and Infrastructure: AWS (S3, EC2, Lambda, CloudWatch, Fargate, EKS/ECS), Azure, GCP, Databricks, Docker, Kubernetes, Terraform, CI/CD, Airflow, and Kafka.
- Enterprise Integration: Demonstrated experience integrating production software or AI systems with one or more enterprise platforms using APIs, event\-driven interfaces, batch or data pipelines, authentication and authorization, and robust error\-handling patterns.
- Operational Excellence: Load balancing, monitoring and alerting (Datadog, Grafana, LangFuse), debugging production issues, evaluation and release discipline, and cost and performance optimization.
- Preferred Platform Experience \- Strong Plus: Experience with one or more of SAP, SAP S/4HANA, SAP Ariba, enterprise resource planning (ERP) platforms, Ivalua, OneStream, Darwin Analytics, SupplyOn, or SAP Integrated Business Planning (IBP). Experience using or extending platform\-native AI capabilities is valuable; breadth across every platform is not required.
- Preferred Supply Chain Domain Experience \- Strong Plus: Delivery experience in one or more of procurement, supplier collaboration and management, risk management, quality, costing, engineering, materials and warehouse management, finished\-goods or component\-level planning, and ESG. Familiarity with automotive supply chain and compliance concepts such as Extended Producer Responsibility (EPR), Process Release Audit (PRA), and Production Part Approval Process (PPAP) is an additional advantage.
- Soft Skills: Strong communication abilities, product\-oriented thinking, effective collaboration with technical and functional subject\-matter experts, and the capacity to learn and adapt quickly in a dynamic environment.
- Education: BS, MS, or PhD in Computer Science, Electrical 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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