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Senior Llm Developer Jobs in Michigan (NOW HIRING)

Corning

Role Purpose Are you passionate about building AI agents and LLM-powered solutions that can ... As a Senior AI Engineer - Cybersecurity , you will design, build, and deploy AI-powered solutions ...

FastTek

Commodity Senior Engineer

Dearborn, MI

$112K - $148K/yr

... * DevOps: Git, Docker, CI/CD, Cloud Build, Artifact Registry, Kubernetes, automated deployments * AI/LLM: AI coding assistants, prompt engineering, LLM APIs, RAG, embeddings, vector databases ...

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Senior Llm Developer information

What is a senior LLM developer?

Senior LLM Developers are experienced software engineers who specialize in building, fine-tuning, and deploying large language models (LLMs) such as GPT, BERT, or similar AI models. They work on advanced natural language processing (NLP) tasks, optimize model performance, and often lead teams in developing AI-driven applications. Their responsibilities include data pipeline development, model training, performance evaluation, and integrating LLMs into products. They are proficient in programming languages like Python, familiar with machine learning frameworks, and stay updated with the latest research in AI and NLP.

What are the key skills and qualifications needed to thrive as a senior LLM developer?

To thrive as a Senior LLM Developer, you need deep expertise in natural language processing, machine learning, and advanced programming skills, typically supported by a relevant degree and experience with large language models. Proficiency with frameworks like PyTorch or TensorFlow, cloud platforms (AWS, GCP, Azure), and version control systems, as well as familiarity with model fine-tuning and deployment, are essential. Strong problem-solving, communication, and collaboration skills help in leading teams and translating complex requirements into innovative solutions. These skills are crucial for building, optimizing, and maintaining robust language models that meet organizational objectives and stay ahead in a rapidly evolving field.

What are some common challenges senior LLM developers face when deploying large language models in production environments?

Senior LLM Developers often encounter challenges such as optimizing model performance for latency and scalability while maintaining accuracy. Managing resource-intensive inference and ensuring robust monitoring to detect issues like model drift or biased outputs are also key concerns. Additionally, integrating LLMs with existing systems and coordinating with cross-functional teams, such as MLOps engineers and product managers, is essential for successful deployment. Staying updated with rapidly evolving frameworks and compliance requirements adds to the complexity of the role.

What is the difference between Senior Llm Developer vs Machine Learning Engineer?

AspectSenior Llm DeveloperMachine Learning Engineer
CredentialsAdvanced degrees in CS, NLP, or AI; experience with LLMsDegrees in CS, Data Science, or related fields; experience with ML frameworks
Work EnvironmentResearch labs, AI startups, tech companies focusing on NLPTech companies, startups, industries applying ML solutions
Industry UsagePrimarily in NLP, AI research, and language model developmentBroader across AI applications, including vision, speech, and data analysis

While both roles require strong AI and ML knowledge, Senior Llm Developers specialize in language models and NLP, whereas Machine Learning Engineers work across various AI domains. The roles often overlap but differ in focus and application areas.

What are the most commonly searched types of Llm Developer jobs in Michigan?

The most popular types of Llm Developer jobs in Michigan are:

What are popular job titles related to Senior Llm Developer jobs in Michigan?

For Senior Llm Developer jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Senior Llm Developer jobs in Michigan look for?

The top searched job categories for Senior Llm Developer jobs in Michigan are:

What cities in Michigan are hiring for Senior Llm Developer jobs?

Cities in Michigan with the most Senior Llm Developer job openings:

Senior Llm Developer jobs near you
Infographic showing various Senior Llm Developer job openings in Michigan as of August 2026, with employment types broken down into 79% Full Time, 5% Part Time, 15% Contract, and 1% Nights. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution.

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 27 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.