Lead AI Engineer (Agentic Systems)
Description
About the Role
Grade Level (for internal use)
11
Lead AI Engineer (Agentic Systems)
Role Summary
As the Lead AI Engineer (Agentic Systems), you will help architect and build the organization’s next generation of autonomous AI workflows. This is a multidisciplinary technical role operating at the intersection of Software Engineering, Data Engineering, and Machine Learning Engineering . 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
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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 .
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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.
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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.
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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.
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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.
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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
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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.
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Real-Time Context Injection: Ensure agents have access to "operational real-time" data (seconds/minutes latency) by optimizing retrieval architectures and vector store performance.
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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
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LLMOps & Tracing: Implement comprehensive observability using tools like Langfuse to trace agent reasoning steps, monitor token usage, and debug latency issues in production.
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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.
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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
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Technical Roadmap Definition: Partner with Product and Engineering leadership to scope feasibility for autonomous projects; define the "Agentic Architecture" roadmap.
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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.
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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
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Experience: 7+ years of total technical experience in Software Engineering, Data Engineering, or Machine Learning.
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GenAI Specialization: 2+ years of specific experience building and deploying LLM-based applications or Agentic Systems in production.
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Database & Lakehouse Mastery: E xperience architecting storage layers for AI, including Vector Databases (e.g., Pinecone, Weaviate , Qdrant ), NoSQL/Relational Databases (PostgreSQL, DynamoDB), and modern Data Lakehouses (specifically Databricks or Snowflake).
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Cloud & Infrastructure: E xpertise in cloud architecture and container orchestration (AWS, GCP, or Azure) using Kubernetes and Docker. You must be comfortable deploying and scaling your own applications.
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LLM Ecosystem: F amiliarity with common LLM frameworks and orchestration libraries (e.g., LangGraph , LangChain , CrewAI , AutoGen ). You understand the mechanics of RAG, embeddings, and context window management.
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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).
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Language Proficiency: Advanced proficiency in Python for systems engineering, capable of writing modular, testable, and maintainable production code.
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Education: Bachelor’s degree in Computer Science , Engineering, Mathematics, or a related technical field.
Preferred
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Advanced Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
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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.
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Graph Technologies: Experience with Knowledge Graphs (e.g., Neo4j, AWS Neptune), Graph Databases, and GraphML (Graph Machine Learning) to support complex reasoning and relationship modeling.
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Agentic Tooling: Specific experience with LangGraph , LiteLLM , Langfuse , AWS AgentCore , or implementing the Model Context Protocol (MCP).
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Advanced Architectures: Proven track record of implementing Agent-to-Agent (A2A) communication, swarm intelligence, or multi-modal agent workflows.
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Real-Time Operations: Experience working in environments requiring operational real-time processing (e.g., FinTech, Energy, Logistics).
Why This Role Matters
You won't just 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 we operate , 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 that represents the absolute cutting edge of the industry.
About S&P Global Energy
At S&P Global Energy, our comprehensive view of global energy and commodities markets enables our customers to make superior decisions and create long-term, sustainable value. Our four core capabilities are: Platts for news and pricing; CERA for research and advisory; Horizons for energy expansion and sustainability solutions; and Events for industry collaboration.
S&P Global Energy is a division of S&P Global (NYSE: SPGI). S&P Global enables businesses, governments, and individuals with trusted data, expertise, and technology to make decisions with conviction. We are Advancing Essential Intelligence through world-leading benchmarks, data, and insights that customers need in order to plan confidently, act decisively, and thrive economically in a rapidly changing global landscape. Learn more at www.spglobal.com/energy .
What’s In It For You?