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Manager Ai Agent Engineer Jobs in Michigan (NOW HIRING)

AI/RPA Engineer

Kalamazoo, MI · On-site

$175K - $200K/yr

... AI Agent framework supporting: • Task automation • Data retrieval and summarization • ... LLM Engineering & MLOps • Implement and manage LLM integrations including: • Secure prompt ...

AI Engineer

Detroit, MI · On-site

$50K - $112K/yr

... agent harnesses and implementing context engineering, memory management, retry logic, and structured output validation to support reliable, multi-step AI workflows - Demonstrating proficiency in ...

AI Engineer

Grand Rapids, MI · On-site

$50K - $112K/yr

... agent harnesses and implementing context engineering, memory management, retry logic, and structured output validation to support reliable, multi-step AI workflows - Demonstrating proficiency in ...

Senior AI Data Engineer

Grand Rapids, MI · On-site

$121K - $151K/yr

... AI agent architectures, including tool calling, orchestration, context management, memory ... Strong programming skills in Python and experience building scalable software services, APIs, and ...

Senior AI Data Engineer

Grand Rapids, MI · On-site

$121K - $151K/yr

... AI agent architectures, including tool calling, orchestration, context management, memory ... Strong programming skills in Python and experience building scalable software services, APIs, and ...

Experience with AI/agent frameworks or tools such as LangChain, LlamaIndex, CrewAI, AutoGen ... management, incident response, or threat intelligence platforms . What we offer: Great benefits ...

Senior Platform Engineer

Ann Arbor, MI

$102K - $140K/yr

... LLM/Generative AI infrastructure (e.g. managed agent runtimes, LLM gateways, RAG or document ... Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to ...

Midlevel AI Developer

Ann Arbor, MI · On-site

$50 - $55/hr

... managers, architects, and data teams-to build scalable AI solutions utilizing modern machine learning, generative AI, agent frameworks, and enterprise-grade software engineering practices. In this ...

AI Agent & Chatbot Projects * AI Workflow Projects * Predictive Modeling Projects * Educate Finance ... Project Documentation and Change Management * Finance SharePoint Site Administration * Ad Hoc ...

Designing and delivering embedded artificial intelligence (AI) agent capabilities within Oracle ... Bachelor's degree or higher in computer science, information technology, software engineering ...

Full-Stack Engineer

Ann Arbor, MI · On-site

$140K - $220K/yr

... AI agent. Many startups are attempting to attack this problem because the market is so big - $350B ... Creating scalable and reliable solutions aimed at optimizing revenue cycle management. * Working ...

Full-Stack Engineer

Birmingham, MI · On-site

$140K - $220K/yr

... AI agent. Many startups are attempting to attack this problem because the market is so big - $350B ... Creating scalable and reliable solutions aimed at optimizing revenue cycle management. * Working ...

Showing results 41-60

Manager Ai Agent Engineer information

What is the difference between Manager Ai Agent Engineer vs Data Scientist?

AspectManager Ai Agent EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with AI/ML toolsBachelor's or Master's in CS, Statistics, or related fields; proficiency in data analysis and modeling
Work EnvironmentDeveloping, deploying, and managing AI agents; cross-functional teamsAnalyzing data, building models, and deriving insights; research-focused
Employer & Industry UsageTech companies, AI startups, enterprises implementing AI solutionsTech firms, finance, healthcare, research institutions

The Manager Ai Agent Engineer focuses on leading AI agent development and deployment, managing teams, and ensuring AI solutions meet business needs. In contrast, Data Scientists primarily analyze data, build predictive models, and generate insights. Both roles require strong technical skills, but their core responsibilities and work environments differ significantly.

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AI Agent Trajectory Annotator and Reviewer

Bespoke Labs

Grand Rapids, MI • On-site

$20 - $30/hr

Full-time

Posted 5 days ago


Job description

Type: Contract, hourly

Location: Remote

Hours: 20–30 per week

Pay: $20–30/hour, based on experience and language coverage

Start: Immediate

ABOUT THE ROLE

We evaluate how well advanced AI coding agents solve real engineering problems. An agent is given a real open source codebase inside a container and a hard task, then works on its own for 80 to 250 steps. A trajectory is the full record of that run — every command, result, and decision.

You will do two jobs, and you should expect either on any given day.

•Annotate — Read a trajectory nobody has looked at yet and judge it step by step.

• Review — Take an existing annotation, written by our AI tooling or another person, and confirm, correct, or reject it.

TASKS YOU'LL SEE

• Feature build — Add a working feature to a live library without breaking anything that already worked.

• Rebuild — Work out what a compiled tool does by running it, then rebuild it to match its output, exit codes, and file effects.

• Bug hunt — Find and fix twenty undocumented bugs across a dozen files with no test suite, then record what caused them.

Mostly Python and Go, with some Rust, C, and Ct+. A trajectory runs about 100 steps.

WHAT YOU JUDGE IN A TRAJECTORY

• Was the command right for the state the environment was actually in?

• Did the agent read the previous output correctly?

• Was the step wrong, or only inefficient — these are scored differently.

• Where did the run first go off course — usually earlier than where it visibly broke.

• Did the agent notice its own mistake and recover, or keep building on a false assumption?

• Did it game the grader instead of solving the task (e.g., weakening a test or hardcoding an expected value)?

WHAT WE NEED FROM YOU

• Experience — 2+ years in software engineering, DevOps, or site reliability, with real debugging in real codebases.

• Languages — Strong in Python or Go, and able to read a language you've never used.

• Linux — Comfortable with logs, running processes, build failures, and containers.

• Workflow — Everyday Git, diffs, pull requests, and issue tracking.

• Debugging — Able to work with no test suite and no error message pointing at the cause.

• Focus — Able to hold context across a long run, because step 74 can depend on step 12.

• Writing — Clear English, since every judgement needs an explanation another engineer can check.