As the Lead AI/ML Engineer (Agentic Systems), you will architect and deliver production-grade autonomous AI workflows that go well beyond conversational assistants. This role sits at the intersection of software engineering, data engineering, and machine learning engineering, building stateful, goal-driven AI systems that can reason, plan, coordinate, and execute complex tasks with appropriate controls.

1) Agentic Systems Architecture & Core EngineeringDesign and build multi-agent workflows: Lead hands-on engineering of stateful agentic applications using agent orchestration frameworks capable of coordinating multiple autonomous components.Agent-to-agent collaboration: Define and implement robust communication patterns that allow agents to delegate sub-tasks, negotiate execution paths, and coordinate outcomes in dynamic environments.State, memory, and long-running execution: Engineer control flows for non-deterministic systems, including message passing, persistent memory, recoverability, and interruptible execution for long-running tasks.Standardized tool interfaces: Establish universal interfaces between agents, enterprise data sources, and operational tools to ensure modularity, reusability, and consistent governance.Model integration and runtime optimization: Build routing and fallback strategies across multiple model endpoints; optimize context management, latency, and inference cost while maintaining reliability.Production deployment: Package and deploy workloads via containerization and cluster orchestration, using cloud-native services for scaling, isolation, and secure runtime operations.

2) Data Engineering & Operational Real-Time IntegrationBuild agent-ready data pipelines: Develop and maintain high-throughput ingestion and transformation pipelines that convert raw operational signals into structured, machine-consumable context.Real-time context injection: Ensure agents can access near-real-time operational data by designing efficient retrieval patterns and optimizing vector databases and associated retrieval architectures.Cross-functional execution: Serve as the technical bridge between AI and data teams—translating agent needs into schemas, data contracts, SLAs, and pipeline specifications, while resolving bottlenecks hands-on.

3) Observability, Governance & Human-in-the-LoopLLMOps, tracing, and debugging: Implement end-to-end observability for agent execution, including reasoning traces, performance telemetry, cost monitoring, and production debugging workflows.Safety and control frameworks: Design hybrid autonomy modes (human-in-the-loop through fully autonomous), including approval gates, policy enforcement, and “break-glass” controls for sensitive operations.Evaluation and reliability standards: Establish rigorous testing strategies for stochastic systems; automate evaluation pipelines to measure accuracy, failure modes, drift, and regression risk prior to deployment.

4) Technical Leadership & StrategyDefine the agentic architecture roadmap: Partner with product and engineering leadership to scope feasibility, set technical direction, and prioritize high-impact autonomous initiatives.Mentorship and engineering standards: Set expectations for code quality, architectural patterns, and review processes; mentor engineers to level up agentic engineering practices.Innovation to production: Rapidly prototype emerging approaches (e.g., advanced retrieval strategies, graph-based reasoning patterns) and mature successful experiments into supported production capabilities.