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Ai Rag Jobs in Rochester Hills, MI (NOW HIRING)

Deloitte

Google AI Architect

Detroit, MI

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

New

FTE Factory Advisors

AI Systems Engineer

Detroit, MI · On-site

Do you enjoy designing the systems behind AI agents, RAG applications, and data pipelines that run in real environments with data, security, and reliability constraints? If you're energized by ...

X by 2

Technical Lead (Applied AI)

Detroit, MI · On-site

Agentic SQL retrieval, MCP integration, agentic tool use, as well as vector databases & RAG ... AI Evaluation & Production Readiness : defining evaluation methods, testing model behavior ...

Versigent

AI Build Engineer Manager

Troy, MI

You are a hybrid architect developer who excels at translating complex AI concepts-such as Agentic workflows, orchestration patterns, and RAG architectures-into "Golden Path" reference ...

Deloitte

AI Engineer Consultant

Detroit, MI · Hybrid

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Judge Group, Inc.

AI Specialist

Pontiac, MI · On-site

$60 - $65/hr

Copilot, LLMs, RAG, Enterprise AI Platforms * Big Data: Apache Spark, Databricks * Cloud: AWS (SageMaker, EC2, S3), Azure Machine Learning, Azure Databricks * Databases: SQL, NoSQL * Visualization:

Deloitte

Google AI Lead Architect

Detroit, MI

$54.75 - $75/hr

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

ALTEN Technology USA

Practice Manager - AI & Data

Troy, MI · On-site

$160K - $190K/yr

Generative AI & LLM ecosystems (prompt engineering, RAG, multi-agent systems) * Data Engineering & Modern Data Platforms (ETL/ELT, streaming, data lakes, data mesh) * Cloud-based AI architectures ...

ALTEN Technology USA

Practice Manager - AI & Data

Troy, MI · On-site

$160K - $190K/yr

Generative AI & LLM ecosystems (prompt engineering, RAG, multi-agent systems) * Data Engineering & Modern Data Platforms (ETL/ELT, streaming, data lakes, data mesh) * Cloud-based AI architectures ...

Versigent

AI Build Engineer Manager

Troy, MI · On-site

You are a hybrid architect developer who excels at translating complex AI concepts-such as Agentic workflows, orchestration patterns, and RAG architectures-into "Golden Path" reference ...

Stellantis

AI & Automation Engineer

Auburn Hills, MI · On-site

Experience with AI, machine learning, generative AI, large language models, RAG, vector search, prompt engineering, or AI-assisted software development. * Experience developing dashboards, web ...

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Showing results 21-40

All JobsAi Rag JobsAi Rag Jobs in Rochester Hills, MI

Ai Rag information

See Rochester Hills, MI salary details

$29.5K

$53.6K

$76.9K

How much do ai rag jobs pay per year?

As of Aug 9, 2026, the average yearly pay for ai rag in Rochester Hills, MI is $53,612.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,100.00 and $59,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
What are popular job titles related to Ai Rag jobs in Rochester Hills, MI? For Ai Rag jobs in Rochester Hills, MI, the most frequently searched job titles are:
What cities near Rochester Hills, MI are hiring for Ai Rag jobs? Cities near Rochester Hills, MI with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Rochester Hills, MI as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $53,612 per year, or $25.8 per hour.

Principal Engineer - HR AI Solutions

Harman International

Novi, MI • On-site

$140 - $210/hr

Other

Posted 12 days ago

Job description

Artificial Intelligence & Machine Learning

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 for Digital HR and HR Business Partners. You will combine full-stack AI development with strong HR process, data, privacy, and governance awareness to solve business problems across the employee lifecycle, workforce planning, skills intelligence, talent, learning, case management, and employee experience.

This role is not centered on configuring one HR platform. Instead, you will evaluate AI capabilities across tools and vendors, advise HR stakeholders on where AI can create value, and build practical solutions when custom development, orchestration, or integration is the right path. You will architect, develop, and maintain production‑grade systems that may include RAG pipelines, agentic workflows, model routing, vector search, evaluation, guardrails, observability, analytics, and visualizations integrated with enterprise HR data products and internal platforms.

What You Will Do
  • Solve HR business problems with AI: Partner with Digital HR, HR COEs, HRIS, IT, Legal, Privacy, and regional stakeholders to understand business needs and identify where AI can automate work, generate insight, or improve decision support.
  • Act as a trusted AI consultant: Advise HR teams on AI opportunities, risks, implementation options, data readiness, governance requirements, and the trade‑offs between vendor capabilities, configuration, integration, and custom development.
  • Build AI‑enabled HR solutions end to end: Develop prototypes and production solutions such as HR knowledge copilots, employee policy assistants, case triage tools, document summarization, onboarding support, skills intelligence, workforce planning analytics, and AI‑assisted process workflows.
  • Evaluate AI tools vendor‑neutrally: Assess capabilities across HR and enterprise platforms such as Workday, ServiceNow, Microsoft, and emerging AI tools, focusing on concepts, fit, value, and feasibility rather than deep specialization in one system.
  • Design and implement RAG pipelines: Build retrieval solutions over HR policies, job profiles, skills taxonomies, learning content, business rules, case data, requirements documents, lessons learned, and other structured or unstructured HR content.
  • Develop agentic workflows: Use orchestration frameworks and agent patterns to translate HR processes into reliable AI‑enabled workflows with appropriate human review, escalation, and auditability.
  • Create analytics and visualizations: Move beyond static reporting by developing AI‑driven insight generation, workforce skill heat maps, automation and augmentation analysis, replacement‑impact views, and decision‑support tools from integrated HR data products.
  • Implement enterprise‑grade controls: Build guardrails, content policies, safety filters, prompt/version management, model evaluation, latency and throughput tuning, cost controls, fallback strategies, and model‑routing approaches.
  • Protect HR data: Design solutions with privacy, PII protection, role‑based access, employee‑data sensitivity, works council considerations, retention requirements, and model/data governance built in from the start.
  • Operate production solutions: Containerize applications, automate CI/CD, monitor usage and quality, debug production issues, manage observability, and improve cost, reliability, and performance over time.
  • Communicate clearly and iterate quickly: Translate complex AI concepts for non‑technical HR stakeholders, document recommendations, share demos, gather feedback, and build trust through practical value delivery.
What You Need To Be Successful
  • Experience: 8+ years of experience building production software or data products, including hands‑on experience with ML, LLMs, Generative AI, or AI‑enabled workflow automation.
  • AI and GenAI foundations: Strong conceptual and practical understanding of LLMs, embeddings, RAG, agentic workflows, prompt engineering, model orchestration, model evaluation, guardrails, and responsible AI practices.
  • Programming: Proficiency with Python, such as FastAPI, NumPy, Pandas, scikit‑learn, Pydantic, and Jinja2, plus Node.js or TypeScript; strong experience with APIs, distributed systems, and integration patterns.
  • Full‑stack delivery: Ability to build internal applications, dashboards, copilots, and workflow tools using modern front‑end and back‑end patterns, such as React, REST or GraphQL services, and reusable UI/data components.
  • Data and search: Experience with SQL and NoSQL databases, search and analytics platforms,
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