Lead AI Engineer

Agentic Systems Architecture & Core Engineering 

• Architect & Build Multi-Agent Workflows: Lead the hands-on design and coding of stateful, production-grade agentic systems using Python and orchestration frameworks like LangGraph, CrewAI, or AutoGen. 

• Agent-to-Agent (A2A) Communication: Design and implement robust A2A protocols enabling autonomous agents to collaborate, hand off sub-tasks, and negotiate execution paths dynamically within multi-agent environments. 

• State Management & Orchestration: Engineer robust control flows for non-deterministic agents; implement complex message passing, memory persistence, and interruptible state handling to support long-running autonomous tasks. 

• Tool Interface Design (MCP): Implement and standardize the Model Context Protocol (MCP) to create universal interfaces between agents, data sources, and operational tools, ensuring modularity and scalability. 

• Model Integration & Optimization: Utilize proxy services (i.e. LiteLLM) to manage model routing and fallback strategies; optimize context windows and inference costs across proprietary and open-source models. 

• Production Deployment: Containerize agentic workloads using Docker and orchestrate deployments on Kubernetes; leverage AWS AgentCore or similar cloud-native services for scalable infrastructure. 

Data Engineering & Operational Real-Time Integration 

• Build Agent Data Pipelines: Write and maintain high-throughput ingestion pipelines (using Databricks or Python-based ETL) that transform raw operational signals into structured context for agents. 

• Real-Time Context Injection: Ensure agents have access to "operational real-time" data (seconds/minutes latency) by optimizing retrieval architectures and vector store performance.  

• Cross-Functional Engineering: Act as the technical bridge between Data Engineering and AI teams; translate complex agent requirements into concrete data schemas and pipeline specifications, while stepping in to resolve hands-on bottlenecks in data availability. 

Observability, Governance & Human-in-the-Loop 

• LLMOps & Tracing: Implement comprehensive observability using tools like Langfuse to trace agent reasoning steps, monitor token usage, and debug latency issues in production.  

• Safety & Control Frameworks: Design hybrid execution modes ranging from Human-in-the-Loop (HITL) for sensitive operations to fully autonomous execution; build "break-glass" mechanisms and guardrails for automated decision-making.  

• Evaluation & Reliability: Establish technical standards for testing non-deterministic outputs; automate evaluation pipelines to measure agent accuracy, hallucination rates, and drift before deployment. 

Technical Leadership & Strategy 

• Technical Roadmap Definition: Partner with Product and Engineering leadership to scope feasibility for autonomous projects; define the "Agentic Architecture" roadmap. 

• Mentorship & Standards: Define code quality standards, architectural patterns, and PR review processes for the AI engineering team; upskill team members on the latest agentic frameworks and methodologies. 

• Innovation: Proactively prototype with emerging tools (e.g., new reasoning models, graph-based RAG) to solve high-value business problems, moving successful experiments into the production roadmap.