Senior AI Engineer

Summary: Build and maintain the production infrastructure for AI agents, including orchestration, evaluation, and deployment systems. This senior individual-contributor role focuses on creating reliable, scalable platforms that integrate with enterprise systems like SAP and Databricks.

POSITIONSUMMARY:

We
are looking for a Senior AI Engineer to build the systems that make AI agents
do real, reliable work across our client’s company. This is a deep, hands-on,
senior individual-contributor role. You will own the implementation of their
agentic platform — the harness, the agent pipelines, the evaluation
infrastructure, and the production systems that turn AI from a prototype into
something teams depend on every day.

By
"harness" we mean the machinery that makes autonomous AI reliable:
the orchestration loop, the tool and data integrations, the testing and
evaluation gates, the observability layer, and the deployment infrastructure
around the models. Building that machinery — and keeping it running in
production against real data and real workflows — is the job.

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 building the foundation an entire
organization will run AI on for the next decade. The portfolio is messy, the
problems are real, and the mandate is clear.

KEY JOBRESPONSIBILITIES:

Build the harness. Implement the core machinery that makes AI agents reliable:
the orchestration loop, the tool-execution and integration layer, evaluation
gates, state persistence, error recovery, and the observability that makes
agent behavior legible. The models change; the harness you build is what lasts.

Ship agentic systems to production — and keep them reliable. Take AI systems
from prototype to production: deployment, monitoring, rollback, controlled
release, and the self-healing loops that detect and recover from failures. The
bar is not a demo — it is autonomous systems running against real client data
and workflows, reliably, with you accountable for correctness and uptime.

Build the evaluation and verification infrastructure. Verification is
what separates a demo from production. You will build the evaluation harness,
the regression suites, the deterministic gates (tests, linters, contract
checks), and the automated judging infrastructure that lets them prove AI
output is correct before it ships.

Build the platform substrate. Implement the
production components that connect AI agents to our client’s real systems and
data — Autodesk, SAP, Databricks, and a growing set of external services — and
the interfaces people use to work with those agents.

Set the engineering bar by example. As one of the most
senior engineers on the team, you set the implementation patterns, write the
reference code others build on, review at depth, and raise the quality of
everyone working alongside you.

REQUIREDKNOWLEDGE, EDUCATION, EXPERIENCE, SKILLS, AND ABILITIES:

• 10+ years in software engineering, with demonstrated depth as

a senior individual contributor who ships complex systems to production

• Hands-on experience building and operating production AI or

agentic systems — not just using AI tools in your own workflow. You have taken
an LLM or agent-powered system to production and kept it running

• Experience implementing the components behind reliable AI

systems: orchestration, tool/function calling, evaluation, context management,
and handoffs between steps or agents

• Strong software engineering fundamentals — testing, CI/CD,

observability, error handling — applied to the realities of nondeterministic AI
systems

• Production cloud operations, ideally AWS: architecting,

deploying, and operating real workloads

• Data and integration experience: building pipelines that move

data reliably between enterprise systems (ERP, SaaS APIs, data warehouses or
lakehouses)

• A pragmatic builder's instinct: you build the reusable thing,

iterate in production, and know when shipping behind a flag beats perfecting on
paper

• Strong communicator who can work with engineers, managers,

and executives and adjust the level for each

NICE TO HAVE:

• Hands-on experience with an agent framework or SDK and with

multi-agent or planner/generator/evaluator architectures

• Experience building evaluation frameworks or automated

AI-judging systems

• Experience with Databricks or a similar lakehouse platform
• Experience building 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 transformation, or similar

• Open-source contributions or public work that shows how you

build

WHY THISROLE:

You
will build the platform an entire organization runs AI on, with a clear mandate
and a direct line to the Director of APEX. The platform is early — that is the
appeal. You are not maintaining someone else's infrastructure; you are building
the harness our client will use for the next decade. When the models get
better, the infrastructure you build is what lets the company capture it.