AI Engineer

About the Role:

We are looking for a Senior AI Engineer to act as the technical lead for our Agentic AI product engineering pod. This is a hybrid IC/lead role. Beyond remaining deeply hands-on, this role is responsible for owning the day-to-day technical guidance, task allocation, growth of junior engineers into the domain and upholding engineering standards.

Key Responsibilities:

• Lead the delivery of production AI agent systems, from prototyping agent workflows to deploying and scaling them in production.
• Lead design and code reviews for the pod, ensuring consistent engineering quality and technical standards.
• Directly manage 1-2 junior engineers: assign and prioritize work, run regular 1:1s, give ongoing feedback, and support their career growth.
• Write clean, maintainable, well-tested Python code (FastAPI, Pydantic, async) and set the standard for code quality across the team.
• Unblock the team on technical issues and act as the first escalation point before involving the engineering manager.
• Establish and document engineering best practices for system design, prompt/context strategies, and evaluation methodology.
• Drive evaluation standards for non-deterministic LLM outputs across the team (eval design, regression testing, red teaming).
• Partner directly with product and business stakeholders to translate ambiguous requirements into scalable agent system design.
• Evaluate frontier and open-source LLMs, agent frameworks, and tooling; make and defend adoption recommendations to leadership.

Requirements:

AI/ML and Agent Engineering

• Strong machine learning foundation, including deep understanding of tokenization, context windows, embeddings, and transformer architectures.
• Hands-on production experience with agent design patterns (ReAct, planning, reflection, tool use, memory, multi-agent handoff).
• Deep hands-on experience with RAG frameworks, LangChain/LangGraph or AutoGen for product development, including A2A and MCP integration at scale.
• Hands-on experience with Azure AI services for agent orchestration (e.g. Azure AI Foundry, Prompt Flow, Azure OpenAI Service).
• Advanced experience in prompt and context engineering, with a track record of driving eval methodology and testing strategy for non-deterministic outputs across a team.

Software Engineering

• 5 years of experience in Python product development, with deep proficiency in frameworks/libraries.
• Experience owning cost/latency tradeoffs and optimization for LLM systems at production scale.
• Experience using AI coding assistants (e.g. Cursor, Claude Code) in daily development workflows.

Technical Leadership

• Experience leading design/code reviews and setting technical standards for a team.
• Comfortable making and defending technical tradeoffs to non-technical stakeholders and leadership.
• Experience mentoring engineers, with genuine interest in growing junior talent.
• Ability to break down and delegate work appropriately based on team members' skill level.
• Comfortable giving direct, constructive feedback and running regular 1:1s.
• Strong collaboration skills with product, engineering, and domain experts to translate business requirements into agent capabilities.

Good to Have

• Knowledge graph (KG) experience, including hands-on work with Neo4j, with the ability to apply KG patterns in agent grounding contexts.
• Hands-on experience fine-tuning open-source LLMs, particularly with parameter-efficient methods (LoRA/QLoRA) and/or supervised fine-tuning (SFT); exposure to RLHF or DPO is a plus.

What Success Looks Like in This Role

• Production AI agent systems are delivered reliably, with sound architecture that scales as usage grows.
• The junior engineers on the pod grow in capability, autonomy, and confidence under this person's mentorship.
• The team maintains high, consistent engineering standards across code, design, and evaluation practices.
• Leadership trusts this person's technical judgment on architecture, tooling, and framework decisions.