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Lead AI Engineer; Agentic Systems

Job in 243601, Gurgaon, Uttar Pradesh, India
Listing for: S&P Global
Full Time position
Listed on 2026-03-03
Job specializations:
  • IT/Tech
    AI Engineer, Systems Engineer
Job Description & How to Apply Below
Position: Lead AI Engineer (Agentic Systems)
This job is with S&P Global, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

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     Lang Graph  ,     CrewAI  , or     Auto Gen  .

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:
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     Agent Core     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 "operational real-time" data (seconds/minutes latency) by     optimizing     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 execution; build "break-glass" mechanisms and guardrails for automated decision-making.

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
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…
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