ZipRecruiter

Log In

1

Rag Llm Jobs in Rochester, MI (NOW HIRING)

Stellantis

Full Stack Engineer

Auburn Hills, MI

Exposure to Generative AI / RAG / LLM-enabled applications Our Benefits -- Designed with You in Mind Comprehensive Health & Well-being Coverage From your very first day, you'll have access to medical ...

Deloitte

AI Engineer Consultant

Detroit, MI · Hybrid

Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry. * Deliver governed datasets and feature ...

X by 2

Technical Lead (Applied AI)

Detroit, MI · On-site

... RAG techniques implementing retrieval-augmented generation patterns using vector stores (e.g., Pinecone, Weaviate, pgvector, ChromaDB), . * LLM Prompt Engineering & Fine-Tuning: designing and ...

Stellantis

Principal AI Engineer

Auburn Hills, MI · On-site

Architect and implement end-to-end LLM systems , including: * Prompt pipelines * Agent-based architectures * Retrieval-Augmented Generation (RAG) systems * Internal AI services and APIs * Integrate ...

Deloitte

Google AI Lead Architect

Detroit, MI · On-site

$54.75 - $75/hr

... LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring. * Build RAG and ...

Deloitte

Google AI Architect

Detroit, MI · On-site

... LLM solutions with Gemini on Vertex AI (prompt/tool/function calling, safety policies, Vector Search, evaluation); implement deployment, inference optimization, and monitoring. * Build RAG and ...

next page

Showing results 1-20

All JobsRag Llm JobsRag Llm Jobs in Rochester, MI

Rag Llm information

See Rochester, MI salary details

$41.4K

$69.3K

$101.3K

How much do rag llm jobs pay per year?

As of Aug 11, 2026, the average yearly pay for rag llm in Rochester, MI is $69,310.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,100.00 and $80,100.00 per year, depending on experience, location, and employer.

What is the difference between Rag Llm vs Data Scientist?

AspectRag LlmData Scientist
Required CredentialsTypically a master's or PhD in AI, machine learning, or related fieldsUsually a master's or PhD in data science, statistics, or computer science
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, tech firms, consulting
Industry UsageAI research, natural language processing, machine learning projectsData analysis, predictive modeling, data-driven decision making

Rag Llm and Data Scientist roles often overlap in AI and data analysis fields, but Rag Llm focuses more on language models and AI research, while Data Scientists handle broader data analysis and modeling tasks. Both require advanced degrees and work in tech-driven environments, but their core responsibilities differ in scope and application.

What is a RAG LLM?

RAG LLMs, or Retrieval-Augmented Generation Large Language Models, are advanced AI systems that combine the strengths of traditional language models with external data retrieval systems. They work by first searching a relevant database or knowledge base for up-to-date information, and then using a language model to generate responses based on both the retrieved content and their own training. This approach helps LLMs provide more accurate, current, and contextually relevant answers, especially for specialized or rapidly changing topics. RAG LLMs are widely used in customer support, research, and enterprise applications to improve information accuracy and reliability.

How do RAG LLM engineers collaborate with data scientists and product teams to improve retrieval-augmented generation systems?

RAG LLM engineers often work closely with data scientists to fine-tune retrieval mechanisms, optimize model performance, and evaluate system outputs. They also collaborate with product teams to understand user needs, integrate feedback, and ensure the system delivers relevant, accurate information. Regular cross-functional meetings and code reviews are common, fostering a collaborative environment focused on continuous improvement and innovation in response to real-world challenges.

What are the key skills and qualifications needed to thrive as a Retrieval-Augmented Generation (RAG) LLM engineer?

To thrive as a Retrieval-Augmented Generation (RAG) LLM Engineer, you need a strong background in natural language processing, machine learning, and software development, often supported by a degree in computer science or a related field. Familiarity with frameworks like PyTorch, Hugging Face Transformers, vector databases, and cloud platforms, along with experience deploying large language models, is essential. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaboration and innovation in this fast-evolving space. These skills ensure the development of robust, scalable, and accurate retrieval-augmented AI systems that meet real-world information needs.
What job categories do people searching Rag Llm jobs in Rochester, MI look for? The top searched job categories for Rag Llm jobs in Rochester, MI are:
What cities near Rochester, MI are hiring for Rag Llm jobs? Cities near Rochester, MI with the most Rag Llm job openings:
Rag Llm jobs near you

Principal Engineer - Future of Engineering AI Solutions

HARMAN International

Novi, MI • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 days ago

Job description

General information
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: harmancareers@harman.com.
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, gend

Apply