Lead AI Engineer (Agentic Systems)

About the Role:

Grade Level (for internal use):

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 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 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 "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 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 Data Lakehouses (specifically Databricks or Snowflake).
Cloud & Infrastructure: Expertise in 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: Advanced proficiency in Python for systems engineering, capable of writing modular, testable, and maintainable production code.
Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical 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, and GraphML (Graph Machine Learning) to support complex reasoning and relationship modeling.
Agentic Tooling: Specific experience with LangGraph, LiteLLM, Langfuse, AWS AgentCore, or implementing the Model Context Protocol (MCP).
Advanced Architectures: Proven track record of 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

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 EnergyAt 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?

Our Mission:

Advancing Essential Intelligence.

Our People:

We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.

Our Values:

Integrity, Discovery, Partnership

Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.

Benefits:

We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S&P Global.

Our benefits include:

Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized for your time on.
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.

For more information on benefits by country visit: https://spgbenefits.com/benefit-summaries

Global Hiring and Opportunity at S&P Global:

At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets.

Recruitment Fraud Alert:

If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here.

-----------------------------------------------------------

Equal Opportunity Employer

S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment.

If you need an accommodation during the application process due to a disability, please send an email to: EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person.  US Candidates Only: The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. Pay Transparency Nondiscrimination Provision - https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf

-----------------------------------------------------------

20 - Professional (EEO-2 Job Categories-United States of America), IFTECH202.2 - Middle Professional Tier II (EEO Job Group), SWP Priority – Ratings - (Strategic Workforce Planning)

Details

Location

Gurugram, Haryana, India Haryana, India

About the Role:

Grade Level (for internal use):

Lead AI Engineer (Agentic Systems)

Role Summary

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 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 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 "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 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 Data Lakehouses (specifically Databricks or Snowflake).
Cloud & Infrastructure: Expertise in 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: Advanced proficiency in Python for systems engineering, capable of writing modular, testable, and maintainable production code.
Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical 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, and GraphML (Graph Machine Learning) to support complex reasoning and relationship modeling.
Agentic Tooling: Specific experience with LangGraph, LiteLLM, Langfuse, AWS AgentCore, or implementing the Model Context Protocol (MCP).
Advanced Architectures: Proven track record of 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

What’s In It For You?

Our Mission:

Advancing Essential Intelligence.

Our People:

Our Values:

Integrity, Discovery, Partnership

Benefits:

We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S&P Global.

Our benefits include:

Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized for your time on.
Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.

For more information on benefits by country visit: https://spgbenefits.com/benefit-summaries

Global Hiring and Opportunity at S&P Global:

Recruitment Fraud Alert:

-----------------------------------------------------------

Equal Opportunity Employer

-----------------------------------------------------------

20 - Professional (EEO-2 Job Categories-United States of America), IFTECH202.2 - Middle Professional Tier II (EEO Job Group), SWP Priority – Ratings - (Strategic Workforce Planning)