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Associate Ai Agent Developer Jobs in Colorado (NOW HIRING)

TIFIN

Staff AI Engineer

Boulder, CO · On-site

The Staff AI Engineer will architect and operationalize production-grade multi-agent systems to enhance advisor capabilities and streamline investment intelligence workflows. Responsibilities : • ...

Wise Skulls

Platform Engineer IV - AWS / DevOps

Denver, CO · On-site

$54.25 - $74.25/hr

... AWS / DevOps Location: Denver, CO (Hybrid) Duration: 6 months (possibility of extension ... Build and maintain infrastructure for AI agent runtime environments, including compute resources ...

Deloitte

Associate AI Solution Architect

Denver, CO · On-site

$64.75 - $85.50/hr

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate ... Solution and AI Architect - AI & Engineering/EaaS Associate Architect Join our AI & Engineering ...

TIFIN

AI Engineering, TIFIN.ai

Boulder, CO · On-site

$200K - $275K/yr

We're building production-grade multi-agent systems that power advisor copilots, investment intelligence workflows, and autonomous research capabilities. Our AI Engineers architect, build, and ...

Checkr

AI Conversation Designer

Denver, CO · On-site

Own the end-to-end lifecycle of assigned AI agents, including prompt engineering, conversation design, testing, and deployment. * Drive measurable improvements in agent performance through analysis ...

Pwc

AI Engineer

Denver, CO · On-site

$50K - $112K/yr

As an Associate, you will focus on learning and contributing to projects while developing your ... orchestrating AI agent workflows using frameworks such as LangGraph to automate multi-step ...

Capgemini

Platform Engineer VI - Data Services

Denver, CO · On-site

$54.25 - $74.25/hr

... DevOps & CI/CD rest apis AI/ML Charter's Infrastructure Intelligence and Analytics (IIA) team builds and operates the data platform and AI agent infrastructure that powers proactive network ...

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All JobsAssociate Ai Agent Developer JobsAssociate Ai Agent Developer Jobs in Colorado

Associate Ai Agent Developer information

What is the difference between Associate Ai Agent Developer vs Machine Learning Engineer?

AspectAssociate Ai Agent DeveloperMachine Learning Engineer
Required CredentialsBachelor's in CS, AI, or related field; some certificationsBachelor's or Master's in CS, Data Science, or related; advanced certifications
Work EnvironmentTech companies, AI startups, R&D labsTech firms, AI companies, research institutions
Employer & Industry UsageDevelops AI agents, chatbots, virtual assistantsDesigns ML models, algorithms, data pipelines
Common Search & ComparisonOften compared for entry-level AI rolesMore advanced, research-focused roles

The Associate Ai Agent Developer typically focuses on building and maintaining AI agents like chatbots and virtual assistants, often at an entry to mid-level. In contrast, a Machine Learning Engineer develops complex ML models and algorithms, usually requiring more advanced skills and experience. Both roles are vital in AI development but differ in scope, complexity, and specialization.

What are the most commonly searched types of Ai Agent Developer jobs in Colorado?

The most popular types of Ai Agent Developer jobs in Colorado are:

What are popular job titles related to Associate Ai Agent Developer jobs in Colorado?

For Associate Ai Agent Developer jobs in Colorado, the most frequently searched job titles are:

What job categories do people searching Associate Ai Agent Developer jobs in Colorado look for?

The top searched job categories for Associate Ai Agent Developer jobs in Colorado are:

Associate Ai Agent Developer jobs near you
Infographic showing various Associate Ai Agent Developer job openings in Colorado as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

AI Agent Trajectory Annotator and Reviewer

Greeley, CO

Bespoke Labs
See more jobs at Bespoke Labsexternal

$20 - $30/hr

Full-time

Posted 14 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.

Apply