AI Engineer

Covalent Resource Group; Southfield (MI); On-site; USD 140,000 - 200,000; Full time

Job summary

Covalent Resource Group is seeking an AI Engineer to be a core builder on our client’s agentic platform. You will own end-to-end features from prototype to production, integrating with enterprise systems and ensuring reliable AI workflows.

In this role you will write tests, monitor production deployments, and collaborate with senior engineers to improve patterns and scale the platform for real, company-wide use. This is hands-on and production-focused.

Qualifications

• 4–6 years in software engineering with production ownership experience.
• Hands-on experience building with LLMs or AI agents beyond demos.
• Strong fundamentals: testing, version control, debugging, code review.

Responsibilities

• Build agent pipelines and integrations with enterprise systems.
• Ship to production with monitoring, deployment, and controlled release.
• Write tests, contract checks, and evaluation cases for AI outputs.
• Improve patterns by adhering to senior engineers' standards and feedback.
• Grow into harder core platform components and orchestration tasks.

Skills

Software engineering

AI agents

LLMs

Testing and debugging

AWS

Data integration

Production ownership

Tools

Databricks

Agent framework

MCP servers

LLM APIs

Job description

POSITION SUMMARY:

We are looking for an AI Engineer to be a core builder on our client’s agentic platform. You will build the agent pipelines, tool integrations, and production features that make AI agents do real, reliable work across the company — working closely with senior engineers who set the patterns you build against.

This is a hands‑on role for an engineer who wants to build production AI systems, not just demos. You will own features end to end from a working prototype to a component running against real company data and workflows. You will learn the failure modes of AI systems by operating them, get your code reviewed at depth, and steadily take on harder and harder parts of the platform.

This is not a role at a software company, and it is not a role where you write a little code on the side. It is a role helping build the foundation an entire organization will run AI on for the next decade. The portfolio is messy, the problems are real, and the path to grow is clear.

KEY JOB RESPONSIBILITIES:

Build agent pipelines and integrations. Implement the working pieces of our agentic systems — agent pipelines, the tool and data integrations that connect agents to Autodesk, SAP, Databricks, and other systems, and the paths agents call to get real work done.

Ship to production and keep your systems healthy. Take your work from prototype to production: deployment, monitoring, controlled release, and fixing what breaks. The bar is not a demo that runs once — it is a component running reliably against real workflows, with you accountable for the parts you ship.

Write tests and evaluation checks. Verification is what separates a demo from production. You will write the tests, contract checks, and evaluation cases that prove your agents produce correct output before it ships — and the regression checks that catch it when their behavior drifts.

Build against the standard — and help improve it. You work inside patterns set by senior engineers. Building well against them is most of the job; the other part is flagging honestly, with a concrete example, when a pattern doesn’t fit what you’re building. That feedback is how the platform gets better.

Grow into the harder work. This is a growth role. You will pair with senior engineers and steadily take on more of the core platform — a harder integration, an evaluation gate, a piece of the orchestration loop.

REQUIRED KNOWLEDGE, EDUCATION, EXPERIENCE, SKILLS, AND ABILITIES:

• 4–6 years in software engineering, with a track record of shipping features to production and owning them afterward
• Hands-on experience building with LLMs or AI agents — you have built something real with an agent framework, an LLM API, or a tool-calling system, beyond using AI in your own workflow
• Strong software engineering fundamentals — testing, version control, code review, debugging — and the discipline to apply them even when the output is nondeterministic
• Comfort in a cloud environment (AWS preferred): you can deploy, monitor, and troubleshoot a service you built
• Some data or integration experience: calling SaaS APIs, moving data between systems, or working against a data warehouse or lakehouse
• A builder’s instinct: you ship the working thing, iterate in production, and ask for help before spinning for a day

NICE TO HAVE:

• Hands-on experience with an agent framework or SDK
• Experience writing tests or evaluation cases for AI or LLM output
• Experience with Databricks or a similar lakehouse platform
• Experience building or consuming tool‑integration layers (e.g., MCP servers) between AI systems and enterprise applications
• Experience in a non-software-company engineering organization — internal tools, corporate IT, or similar
• Public work — open source, a side project, anything that shows how you build

WHY THIS ROLE:

You will build production AI systems at the moment an organization decides to invest seriously in them — with senior engineers and an architect as people you learn from, not gatekeepers who block you. The platform is early, and that is the appeal. You are not maintaining someone else’s infrastructure; you are helping build the platform the company will run on for the next decade, and growing into owning the hardest parts of it