AI Engineer

Requirements are 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 lead technical conversations 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.