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 likeLangGraphCrewAI 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 stepsmonitortoken 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

• 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. PineconeWeaviateQdrant) 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.LangGraphLangChainCrewAIAutoGen). 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:Bachelors degree in Computer Science Engineering Mathematics ora relatedtechnical field.

Preferred

• Advanced Education: Masters 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 withLangGraphLiteLLMLangfuse 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

Youwontjust be building chatbots here; you will be architecting the organizations 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 designcapable of planning collaborating (A2A) and executing taskswill 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.