Lead AI Engineer (Agentic Systems)
AI summary of the role
Lead the design and build of production-grade autonomous AI workflows (Agentic Systems) at S&P Global Energy, moving beyond chatbots to multi-agent architectures.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Architect and build stateful, production-grade multi-agent workflows using Python and orchestration frameworks like LangGraph, CrewAI, or AutoGen.
- Design and implement Agent-to-Agent (A2A) communication protocols for autonomous collaboration and dynamic task execution.
- Engineer robust state management, memory persistence, and interruptible control flows for long-running autonomous tasks.
- Implement Model Context Protocol (MCP) to create universal interfaces between agents, data sources, and operational tools.
What you’ll bring
- 7+ years of total technical experience in Software Engineering, Data Engineering, or Machine Learning.
- 2+ years of specific experience building and deploying LLM-based applications or Agentic Systems in production.
- Expertise in cloud architecture and container orchestration (AWS, GCP, or Azure) using Kubernetes and Docker.
- Advanced proficiency in Python for systems engineering, capable of writing modular, testable, and maintainable production code.
Technologies
Python · LangGraph · CrewAI · AutoGen · Docker · Kubernetes · AWS · Databricks · Pinecone · Weaviate · Qdrant · PostgreSQL
About S&P Global
Sells credit ratings, market indices (S&P 500, Dow Jones), and financial data/analytics to investors, issuers, banks, and corporates worldwide.
Public · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
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 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. Agent-to-Agent (A2A) Communication: Design and
More from the job 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 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. 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 inter [... source excerpt omitted ...] on: 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. 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 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. Real-Time Context Injection: Ensure agents have access to "ope [... source excerpt omitted ...] ing retrieval 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 like Langfuse to trace agent reasoning steps, monitor token 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 execu
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