Lead AI Engineer

Lead AI Engineer (Agentic Systems)

Role Summary

As the Lead AI Engineer (Agentic Systems), you willhelparchitect and build the organization’s 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

• LLMOps& Tracing: 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 your own applications.

• LLM Ecosystem:Familiarity with common LLM frameworks and orchestration libraries (e.g.,LangGraph,LangChain,CrewAI,AutoGen). You understand the mechanics of RAG, embeddings, and context window management.

• Hybrid Engineering Skillset: A unique blend of Data Science (understanding model behavior, probability, and prompting) and Software Engineering (CI/CD, API design, asynchronous programming, and system reliability).

• Language Proficiency: Advancedproficiencyin Python for systems engineering, capable of writing modular, testable, and maintainable production code.

• Education:Bachelor’s degree in Computer Science, Engineering, Mathematics, ora relatedtechnical field.

Preferred

• Advanced Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.

• NLP Expertise: 5+ years of hands-on experience in Natural Language Processing (NLP), ranging from foundational techniques (e.g., text processing, embeddings, classification) to modern architectures.

• Graph Technologies: Experience with Knowledge Graphs (e.g., Neo4j, AWS Neptune), Graph Databases, andGraphML(Graph Machine Learning) to support complex reasoning and relationship modeling.

• Agentic Tooling: Specific experience withLangGraph,LiteLLM,Langfuse, AWSAgentCore, or implementing the Model Context Protocol (MCP).

• Advanced Architectures: Proventrack recordof implementing Agent-to-Agent (A2A) communication, swarm intelligence, or multi-modal agent workflows.

• Real-Time Operations: Experience working in environments requiring operational real-time processing (e.g., FinTech, Energy, Logistics).

Why This Role Matters

Youwon'tjust be building chatbots here; you will be architecting the organization’s "central nervous system." As the Lead AI Engineer for Agentic Systems, you are bridging the gap between static data models and active decision-making. The autonomous workflows you design—capable of planning, collaborating (A2A), and executing tasks—will fundamentally change how weoperate, moving us from human-dependent processes to self-healing, intelligent systems. This is a rare opportunity to define the standards for Agentic AI in a production environment, working with a stack thatrepresentsthe absolutecutting edgeof the industry.