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Home Based Llm Engineer Jobs in Michigan (NOW HIRING)

Beacon Specialized Living

AI/RPA Engineer

Kalamazoo, MI · On-site

$175K - $200K/yr

... based access, and audit logging Clinical AI & Client Risk Intelligence • Develop and support LLM ... LLM Engineering & MLOps • Implement and manage LLM integrations including: • Secure prompt ...

Stellantis

Principal AI Engineer

Auburn Hills, MI · On-site

We are seeking a Principal AI Engineer with deep, hands-on experience in Large Language Models ... Lead the design, development, and deployment of LLM-based automation solutions across multiple ...

Corning

... Python-based solutions and work with APIs, JSON payloads, model integrations, and LLM-enabled ... You can help connect the unconnected, drive the future of automobiles, transform at-home ...

Stellantis

Principal AI Engineer

Auburn Hills, MI

Architect and implement end‑to‑end LLM systems , including: * Prompt pipelines * Agent‑based ... Drive model evaluation, prompt optimization, and system reliability improvements based on ...

Bright Vision Technologies

Prompt Engineer

Novi, MI · On-site

$76 - $99/hr

Job Summary We are looking for a Prompt Engineer to define and lead the strategy, patterns, and tooling for designing prompts, agentic workflows, and LLM-based application architectures across the ...

Corning

$93K - $122K/yr

... LLM-based solutions, chatbots, RAG solutions, agents, automations, or model-connected workflows ... You can help connect the unconnected, drive the future of automobiles, transform at-home ...

Orbotech

AI Software Engineer|Manufacturing

Ann Arbor, MI · On-site

$111K - $188K/yr

The engineer will develop scalable backend services, prototype intuitive user interfaces, and enable seamless interaction between users and LLM-based systems. This position requires a strong blend of ...

Kla

AI Software Engineer|Manufacturing

Ann Arbor, MI

$111K - $188K/yr

The engineer will develop scalable backend services, prototype intuitive user interfaces, and enable seamless interaction between users and LLM-based systems. This position requires a strong blend of ...

Stefanini Group

AI Engineer

Dearborn, MI

Stefanini is looking for an AI Engineer, Dearborn, MI (Onsite) For quick apply, please reach out ... AI or LLM-based applications. * Proven experience designing and deploying multi-agent or multi ...

New

Stefanini

AI Engineer

Dearborn, MI · On-site

Stefanini is looking for an AI Engineer, Dearborn, MI (Onsite) For quick apply, please reach out ... LLM-based applications.Proven experience designing and deploying multi-agent or multi-service ...

New

Stellantis

AI Validation Engineer

Auburn Hills, MI · On-site

We are seeking a Senior AI Validation Engineer to design and implement testing strategies ... Develop automated test pipelines for LLM-based features, including hallucination detection ...

Deloitte

AI Engineer Consultant

Detroit, MI · Hybrid

... LLM-based experiences. * Partner with the Lead AI Solutions Architect and AI Data Engineer to ... translate Human Capital product needs into secure, scalable technical designs and delivered ...

Corning

... Python-based applications, APIs, JSON integrations, model-connected workflows, and LLM-enabled ... You can help connect the unconnected, drive the future of automobiles, transform at-home ...

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All JobsHome Based Llm Engineer JobsHome Based Llm Engineer Jobs in Michigan

Home Based Llm Engineer information

What is the difference between Home Based Llm Engineer vs Data Scientist?

AspectHome Based Llm EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with machine learning modelsDegree in Data Science, Statistics, or related fields; proficiency in programming and analytics
Work EnvironmentRemote, focused on developing and fine-tuning language modelsRemote or on-site, analyzing data and building predictive models
Industry UsageAI companies, tech firms, research institutionsTech, finance, healthcare, and various industries requiring data analysis

Home Based Llm Engineers focus on developing and optimizing large language models remotely, while Data Scientists analyze data to generate insights. Both roles require strong technical skills and often work in similar industries, but their core responsibilities differ in model development versus data analysis.

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Home Based Llm Engineer jobs near you

Artificial Intelligence Senior Associate (W2 Position)

Dearborn, MI • On-site

Megan soft Inc
IT Services • 11 - 50 employees
Learn more about Megan soft Incexternal

Contractor

Re-posted 25 days ago

Job description

Here is a clean, structured Job Description (JD) based on the provided details, formatted to be clear, professional, and ready to post or share with prospective candidates.

Job Title: Senior AI / LLM Engineer (Artificial Intelligence Senior Associate)

Location: Dearborn, MI (Hybrid: 4 days onsite)

Duration: 12 Months Contract

Only W2 No OPTS 

Position Overview

We are looking for a Senior AI / LLM Engineer to architect and deploy production-grade multi-agent orchestration layers, advanced RAG pipelines, and automated intelligence workflows. This role is closer to distributed systems engineering with a probabilistic component than basic prompt tuning or pure ML research.

You will translate complex business requirements into scalable, observable, and secure AI systems on Google Cloud Platform (GCP), working with frameworks like LangGraph, CrewAI, and LlamaIndex, and containerized backend microservices.

Key Responsibilities

  • Architect & Deploy Multi-Agent Orchestration: Build production-grade multi-agent architectures (orchestrator, NL-to-SQL, visualization, RCA/RAG, report generation, notification agents) using stateful agent frameworks with checkpointing and human-in-the-loop validation.

  • Production RAG Systems: Design and scale Retrieval-Augmented Generation (RAG) pipelines using vector databases (pgvector, Pinecone, Weaviate, Qdrant), hybrid search, reranking, and domain-specific chunking strategies.

  • GCP Infrastructure & Integration: Deploy agent microservices and data pipelines using GCP infrastructure (Vertex AI, Cloud Run / GKE, BigQuery, Pub/Sub, CI/CD with Docker/Kubernetes).

  • LLM Observability & Testing: Build robust evaluation and tracing pipelines using tools like LangSmith, Langfuse, or OpenTelemetry ( golden datasets, LLM-as-judge scoring, latency/cost tracking).

  • Safety & Guardrails: Implement prompt-injection defense, output validation, safe execution of AI-generated code/SQL (least-privilege access, sandboxing), and human approval checkpoints for critical actions.

  • Cost & Latency Optimization: Implement tiered model routing (low-cost filtering models vs. high-capability deep-dive models) and intelligent caching strategies.

  • Integration & Collaboration: Connect validated outputs to enterprise operational platforms (e.g., Salesforce, notification systems, reporting tools) and collaborate with data scientists to turn prototypes into production-ready services.

Qualifications & Skill RequirementsRequired Experience & Education:

  • Education: Bachelor’s degree in Computer Science, Software Engineering, or a related technical field (Master's preferred).

  • Experience: 3+ years in production software engineering, including 1–2+ years actively building ML/AI or LLM-driven applications in production environments.

  • Cloud Expertise: Hands-on experience with Google Cloud Platform (GCP) (BigQuery, Vertex AI, Cloud Run, GKE, Pub/Sub).

  • Language & Backend: Strong proficiency in Python (async/concurrent programming) and backend frameworks (FastAPI, Flask).

  • Agent Orchestration: Practical experience using frameworks like LangGraph, CrewAI, LlamaIndex, or equivalent for multi-step agent workflows.

  • Vector DBs & RAG: Hands-on experience with vector search and databases (pgvector, Pinecone, Qdrant, Weaviate).

  • DevOps & Data: Proficiency with Docker, Kubernetes, CI/CD pipelines, and writing complex SQL queries.

  • LLM Security & Observability: Familiarity with LLM guardrails, sandboxing, and evaluation tooling (LangSmith, Langfuse, OpenTelemetry).

Preferred Qualifications (Nice-to-Have):

  1. Experience designing model-routing pipelines for cost optimization at scale.

  2. Hands-on experience with human-in-the-loop or high-stakes validation checkpoints.

  3. Industry experience with automotive, EV charging, IoT, or connected-vehicle telemetry data.

  4. Familiarity with the Model Context Protocol (MCP) or open integration standards across tools/agents.

  5. Prior experience in 0-to-1 product environments or startups handling fast-evolving requirements.