AI Engineer

Our client is the leader in the Early Childhood Education (ECE) sector, providing the most widely used developmental assessment and curriculum system in the US.

They are early in shaping how AI changes the way they build and operate software. They have active initiatives in AI tooling cost-management and agentic development workflows, but their direction is still forming and will keep evolving as the field does.

They are not looking for someone to execute a fixed plan, they are looking for an experienced practitioner who can validate the direction we are setting, bring expertise we do not have in-house, stay ahead of what is emerging, and help us adjust and grow as the landscape shifts. This person is hands-on enough to build and prove things directly, and senior enough that we trust their judgment on where to place bets.

What you will do
Act as a hands-on technical partner across AI engineering initiatives, currently spanning tooling cost management, model routing and selection, and agentic development workflows.
Pressure-test the approaches they are considering, tell them where they are wrong or where a better path exists, and back it with evidence rather than opinion.
Bring outside expertise and patterns from how other organizations are solving these problems, so they are not learning everything the expensive way.
Track what is emerging across models, tooling, frameworks, and techniques, and translate it into concrete recommendations on what they should adopt, defer, or ignore.
Build and run proofs of concept to validate ideas quickly, then help them decide what to operationalize.
Help mature their agentic development workflow so engineering teams can adopt it, including model routing and token efficiency.
Partner with Infrastructure and Security so that what they build fits their architecture and meets their data privacy obligations.
Help them build internal capability, so the expertise compounds with their team and does not leave when the engagement ends.

Required background
5+ years in software, platform, or ML engineering, with recent, direct experience building and operating LLM-backed systems in production.
Demonstrated breadth across the current AI engineering stack: models and their tradeoffs, inference cost dynamics, gateways and routing, agentic workflows, and the AI coding tool landscape.
A track record of forming a point of view on fast-moving technical decisions and being right often enough to be trusted with them.
Strong hands-on engineering ability. This person builds, benchmarks, and ships, not just advises.
Comfort operating with ambiguity and early-stage direction; able to bring structure rather than wait for it.
Cloud and infrastructure fluency (AWS preferred, including Bedrock).

Strongly preferred
Experience helping an organization adopt AI-first engineering practices, not just individual tool use.
Hands-on model evaluation and routing design, and exposure to AI cost governance.
Experience in regulated data environments.
A habit of staying current through real practice (building, writing, contributing) rather than headlines.

What this role is not
Not a fixed-scope implementation seat. The work will change as their direction evolves, and they want someone who helps drive that change.
Not a data science or model training role. The focus is applied AI engineering, integration, and enablement.
Not a manager. Individual contributor, hands-on, with influence earned through expertise.