Lead AI Engineer (Agentic Systems)

Role Summary

As the Lead AI Engineer (Agentic Systems), you willhelparchitect and build the organizations next generation of autonomous AI workflows. This is a multidisciplinary technical roleoperatingat the intersection of Software Engineering, Data Engineering, and Machine LearningEngineering. You will move beyond simple “chatbots” to design production-grade Agentic Systems: intelligent applications capable of reasoning, planning, and executing complex tasks autonomously.

Responsibilities

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 likeLangGraph,CrewAI, orAutoGen.

• 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:Utilizeproxy services (i.e.LiteLLM)to manage model routing and fallback strategies;optimizecontext windows and inference costs across proprietary and open-source models.

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

Data Engineering Operational Real-Time Integration

• Build Agent Data Pipelines: Write andmaintainhigh-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) byoptimizingretrieval 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

• LLMOpsTracing: Implement comprehensive observability using tools likeLangfuseto trace agent reasoning steps,monitortoken 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:Establishtechnical 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.

Qualifications

Required

• Experience: 7+ years of total technical experience in Software Engineering, Data Engineering, or Machine Learning.

• GenAI Specialization: 2+ years of specific experience building and deploying LLM-based applications or Agentic Systems in production.

• Database Lakehouse Mastery:Experience architecting storage layers for AI, including Vector Databases (e.g., Pinecone,Weaviate,Qdrant), NoSQL/Relational Databases (PostgreSQL, DynamoDB), and modern DataLakehouses(specifically Databricks or Snowflake).

• Cloud Infrastructure:Expertisein cloud architecture and container orchestration(AWS, GCP, or Azure)using Kubernetes and Docker. You must be comfortable deploying and scaling …