AI Engineer

The requirement is rarely fully specified, so part of the job is turning a fuzzy business problem into a reliable agentic system. You are the engineer clients remember.
Responsibilities:
Design Agentic Workflows: Own the architecture of your workstream agent topology, tool design, orchestration, and control flow.
Build Production-Grade Systems: Ship reliable, well-tested agentic code that runs in Fortune 500 production environments.
Design and Own Evals: Build eval harnesses and verification rubrics that hold your workstream to a 99.99% reliability bar.
Run Client Working Sessions: Lead technical discussions with client engineers and product owners.
Raise the Team's Bar: Review code from AI engineers and help them grow into workstream ownership.
Requirements:
2-6 years of software engineering experience, including at least one system you took to production and owned.
Demonstrated experience building with LLMs, multi-agent orchestration, RAG, tool-calling, and prompt design in real applications.
Strong Python and solid engineering practice: testing, code review, deployment, and monitoring.
Experience designing evals or test strategies for non-deterministic systems, or a clear grasp of why that matters.
Ability to work from ambiguous requirements and drive a workstream to a reliable outcome with minimal oversight.
Direct, credible communication: you can run a technical conversation with client engineers on your own.
Production experience on Azure, AWS, or Databricks, and exposure to enterprise systems of record such as SAP, Oracle, or Snowflake.
Experience in a regulated enterprise domain: banking, MedTech, CPG, or industrial.
Prior client-facing or consulting-adjacent delivery experience.
Contributions to agentic tooling, open source, or eval frameworks.
2+ years in AI/ML engineering or applied ML.
Strong hands-on experience building and shipping AI systems.

Skills
Generative AI, Machine Learning, Python

Experience
2-6 yrs