Lead AI Engineer - ML/LLM Ops & Agentic AI

Job summary

Responsibilities

At Shift, we are building the next generation of AI-driven platform capabilities. We are looking for an experienced, hands-on Lead AI Engineer in our Data & AI Platform Engineering team to drive our LLMOps roadmap and our most business-critical agentic use cases; someone who is deeply passionate…

• Build agentic systems (hands-on): design and ship production-grade agentic workflows (LLM orchestration, tool calling, MCP, RAG, structured outputs), and define reference…
• Own LLMOps standards: evaluation harnesses, prompt and agent versioning, tracing (OpenTelemetry), cost and token governance, and release gates, with tenant-aware patterns that…
• Evolve our ML platform: be the technical owner of our Databricks-based ML platform (Unity Catalog, MLflow, CI/CD to serving on AKS, monitoring), and extend it toward the agent…
• Run & Serve Open-Weight Models: Partner closely with our Cloud Ops team to design and deploy the architecture required to self-host open-weight foundation models internally.
• Push the frontier: run time-boxed POCs on emerging techniques, such as relational foundation models for claims fraud detection or agentic ingestion of medical billing sources to…

What they're looking for

• Agentic AI expertise: proven experience building production agentic systems (Microsoft Agent Framework, OpenAI Agents SDK, PydanticAI, LangGraph or custom), and strong command of…
• MLOps and engineering: high proficiency in Python; hands-on experience with experiment tracking, model registry, serving and monitoring (ideally Databricks and MLflow); backend…
• Applied ML curiosity: solid ML foundations, ability to judge new approaches critically; exposure to graph ML, fraud or claims data is a plus.
• Software design: high proficiency in Python and strong experience with OOP; proven ability to design robust, maintainable software systems and write clean code, applying the…
• Leadership: experience leading technical initiatives across stakeholders, and coaching or managing technical contributors (or a clear motivation to do so).

Tech stack

• RAG
• Databricks
• MLflow
• LangGraph
• Python
• Azure
• Kubernetes
• Terraform