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Ai Rag Jobs in Michigan (NOW HIRING)

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

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 ...

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 ...

You will architect, develop, and maintain production-grade systems encompassing RAG pipelines ... and AI systems that support GenAI use cases including RAG, agentic workflows, and model ...

Contribute to projects such as testing AI chat assistants and copilots, validating AI agent workflows, evaluating retrieval augmented generation (RAG) search quality, automating AI response ...

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 ...

Google AI Lead Architect

Detroit, MI · On-site

$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 ...

Experience building RAG-based systems, vector databases, and semantic search architectures. * Demonstrated ability to lead large-scale AI initiatives and influence technical strategy. * Deep ...

AI Engineer (W2 Position) Location : Dearborn, MI (Hybrid) Duration: 12+ Months Experience: 8+ ... Experience with RAG, Lang Graph, NLP to SQL, ADK, and A2A Behavioral and Technical Interviews ...

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 ...

Build AI-powered applications that support engineering, operations, manufacturing, and business ... Familiarity with Retrieval-Augmented Generation (RAG), Vector databases, Cloud platforms, Docker ...

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Showing results 1-20

Ai Rag information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

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.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer, AI research director, or executive roles like AI CTO. These roles often require advanced skills in data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

What are AI RAGs?

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.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

What are some common challenges faced by AI RAG (Retrieval-Augmented Generation) 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 Michigan? For Ai Rag jobs in Michigan, the most frequently searched job titles are:
What cities in Michigan are hiring for Ai Rag jobs? Cities in Michigan with the most Ai Rag job openings:
Technical Lead (Applied AI)

Technical Lead (Applied AI)

X by 2

Detroit, MI • On-site

Full-time

Re-posted 11 days ago


Job description

X by 2 is a technology consulting firm specializing in healthcare and insurance transformation. For over 25 years, we have partnered with leading organizations across North America — from strategy and architecture to system modernization, data analytics, and AI — delivering high-impact solutions from start to finish. We're agile, collaborative, and deeply committed to doing consulting the way it was meant to be done.

We are looking for a Technical Lead who will lead the design and development of AI solutions within enterprise environments for our clients. You'll combine hands-on engineering with leadership, guiding architecture decisions, mentoring teams, and building solutions.

Responsibilities
  • Technical Leadership & Architecture

    • Lead architecture, design, development, testing, and deployment of enterprise software solutions (applications, data, integration, AI agents, AI models)

    • Participate in strategy and roadmap discussions, architecture definition, and technology evaluations

    • Mentor engineers through design reviews, code reviews, and technical guidance

    • Quickly evaluate and adopt new tools, technologies, and platforms to build prototypes and proofs-of-concept

    • Drive adoption of AI solutions and collaborate with engineering teams, product leaders, and domain experts to deliver results

  • Software Engineering

    • Design, develop, and maintain scalable, production-grade enterprise applications using modern languages and frameworks (Python, Java, C#, JavaScript)

    • Define and enforce coding standards, best practices, and design patterns across the team

    • Build and maintain CI/CD pipelines, infrastructure-as-code, and cloud-deployed services (AWS, Azure)

    • Integrate enterprise systems via APIs, event-driven architectures, and messaging platforms

    • Identify and resolve performance bottlenecks, technical debt, and system reliability issues

  • Data & Analytics

    • Work with large, complex datasets and ensure data quality and integrity

    • Analyze data to generate insights that inform both model development and broader solution strategy

    • Collaborate with stakeholders to translate business problems into data-driven solutions

  • AI / Machine Learning Development

    • Design, develop, and train machine learning and deep learning models for healthcare and insurance use cases

    • Perform data modeling and feature engineering to support model development

    • Develop custom model metrics and approaches tailored to specific business problems

    • Ensure models are scalable, reliable, and integrated into production systems

  • Agentic AI Development

    • Design and develop LLM-powered workflows and agentic systems that help users complete complex tasks, retrieve information, reason over enterprise data, or interact with internal systems.

    • Integrate LLMs with tools, APIs, databases, documents, and enterprise platforms using patterns such as function calling, MCP, RAG, and structured tool use.

    • Architect orchestration patterns for planning, task decomposition, memory, context management, and human-in-the-loop review where appropriate.

    • Develop orchestration layers to manage agent planning, memory, task decomposition, and execution loops

    • Evaluate agentic systems for correctness, reliability, safety, observability, auditability, and harden them for production readiness.

    • Stay current with emerging AI frameworks and platforms, selecting tools pragmatically based on client needs.

Qualifications
  • Experience

    • 6+ years of experience in software engineering designing and developing enterprise applications, data/analytics solutions, and/or integration solutions

    • 1+ years of providing technical leadership as Tech Lead, Lead Engineer, or Architect

    • 1+ years in developing AI agents and/or AI model development and training

  • Education

    • Bachelor's Degree in Computer Science, Software Development, Software Engineering, or Computer Engineering

    • (Optional) Master's Degree or Minor in AI/Machine Learning or Statistics

  • AI Engineering Skills (Two or More)

    • Agentic AI: building and integrating autonomous AI agents using LLM APIs and orchestration frameworks (e.g., Anthropic Claude, OpenAI GPT, LangChain, CrewAI, AutoGen, MCP)

    • Machine Learning & Deep Learning: developing and training models using standard ML/DL frameworks (e.g., TensorFlow, PyTorch, scikit-learn)

    • Data Engineering & Feature Engineering: building data pipelines, performing feature engineering, and ensuring data quality across large, complex datasets

    • Context Engineering: Agentic SQL retrieval, MCP integration, agentic tool use, as well as vector databases & RAG techniques implementing retrieval-augmented generation patterns using vector stores (e.g., Pinecone, Weaviate, pgvector, ChromaDB), .

    • LLM Prompt Engineering & Fine-Tuning: designing and evaluating effective prompts, system instructions, and fine-tuning strategies for production LLM applications

    • AI Evaluation & Production Readiness: defining evaluation methods, testing model behavior, monitoring performance, and addressing reliability, safety, explainability, and auditability concerns

  • What We're Looking For

    • You move fluidly between writing code and leading a room — equally comfortable in a design review as you are in a pull request

    • You have strong opinions about architecture and aren't afraid to share them, but you know when to listen and adapt

    • You take ownership seriously — on small teams, your decisions have real impact and you're energized by that, not intimidated

    • You're genuinely curious about AI, not just checking a box — you've experimented with agentic systems, LLMs, or ML tools on your own terms

    • You communicate clearly with both engineers and non-technical stakeholders, and can translate complexity without dumbing it down

Location
  • Metro Detroit or North Carolina Research Triangle (Raleigh, Durham, Chapel Hill)

  • Hybrid: flexibility to work remotely, but not fully remote

  • Travel to X by 2 offices (Farmington Hills, MI) and client sites (within US) is required when requested

Work Environment and Culture
  • Work alongside smart, collaborative people who continually challenge and invest in each other

  • Partner with seasoned architects to solve hard problems and challenge assumptions

  • Small teams mean real ownership, with growth and responsibilities driven purely by individual performance

  • Everyone has a voice and is encouraged to shape the company by sharing their interests, ideas, and feedback

Compensation & Benefits
  • Base salary $143K–$185K, depending on experience, plus profit sharing

  • Annual raises and promotions based on performance

  • 401(k) with employer match

  • Comprehensive health, vision, dental, life, and disability insurance coverage, plus voluntary benefits and HSA with employer contribution

  • Home Office Reimbursement, Health and Wellness Reimbursement, and Professional Dress Allowance

  • Professional Self-Development Program

  • Paid vacation, unlimited sick days (as needed), and holidays

  • Company-sponsored social events and an employee recognition rewards program

Sound Like You?
We're not looking for someone who has done everything — we're looking for someone who is driven to. If you thrive in environments where you can move fast, go deep, and make a real difference for clients solving complex problems in healthcare and insurance, we'd love to hear from you.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.


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About X by 2

Sourced by ZipRecruiter

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Farmington Hills, MI, US

Year founded

1998