AI / ML Engineer

Requirements:

• 4-6 years of software engineering experience, including systems you architected and took to production at scale.
• Deep, hands-on experience building agentic or ML systems with LLM orchestration, evaluation, reinforcement learning, and reliability, architecting the stack, not just using it.
• A track record of owning technical direction on ambiguous, high-stakes projects end-to-end.
• Proven ability to design verification and evaluation strategies that hold non-deterministic systems to a high reliability bar.
• Credible senior stakeholder management: you can hold a technical conversation with client engineering leadership and win trust.
• Experience mentoring engineers and setting technical standards for a team.

Strongly Preferred:

• Delivery experience in regulated, high-stakes enterprise domains: banking, MedTech, CPG, or industrial.
• A consulting, forward-deployed, or solutions-architecture background alongside strong engineering.
• Depth in in-environment deployment (Azure, AWS, Databricks), observability, and MLOps for agentic systems, including reinforcement learning pipelines.
• Public work, patents, talks, open source, or writing that shows technical leadership.