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EXL

Analyzes business data and runs outsourced operations

Lead AI Data Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeGurugram, Haryana, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 9 years of total experience, including 2–4+ years of hands-on delivery experience with large language models or generative AI in production use cases.
  • Strong hands-on experience with large language models such as Claude and OpenAI, retrieval-augmented generation pipelines and retrieval optimization, and GPT and agentic AI implementation.
  • Experience with LangChain, LangGraph, or similar frameworks, and with agent orchestration and tool-calling architectures.
  • Deep understanding of large language model limitations, evaluation, and optimization strategies.
  • Strong Python and PySpark engineering expertise for production-grade development, with proven API integration experience.
  • Deep data analysis experience and experience handling large volumes of data.
  • Data engineering integration skills involving Fabric, Azure Databricks, or Snowflake.
  • Exposure to cloud platforms such as Azure, AWS, or GCP; SQL; containers; continuous integration and continuous delivery; and monitoring.
  • Prior experience in at least one of data engineering (ETL/ELT, pipelines, or orchestration), data science or the machine learning lifecycle (especially natural language processing), or analytics engineering or data products.
  • Experience leading solution design or small teams.
  • Ability to translate business problems into AI solutions.
  • Strong stakeholder communication and influencing skills.

Responsibilities

  • Architect enterprise-grade agentic and large language model solutions, including single-agent, multi-agent, and tool-driven workflows.
  • Define scalable generative AI system design patterns, including retrieval-augmented generation, orchestration layers, and evaluation frameworks.
  • Act as the technical anchor for generative AI initiatives across projects.
  • Drive design reviews, architecture governance, and best practices.
  • Design and build agentic systems using large language models for knowledge assistants, document automation and intelligence, and workflow orchestration.
  • Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains.
  • Build tool-calling and function-calling frameworks for agent workflows.
  • Lead end-to-end implementation of retrieval-augmented generation pipelines, covering data ingestion, chunking, embeddings, vector indexing, retrieval, and response generation.
  • Optimize retrieval quality, including recall, relevance, and grounding.
  • Evaluate and benchmark different architectures.
  • Develop production-grade APIs and services using frameworks such as FastAPI or Flask.
  • Drive code quality, testing standards, and reusable architecture components.
  • Ensure solutions are optimized for performance, latency, cost, and reliability.
  • Implement large language model guardrails for hallucination control, safety filtering, and policy enforcement.
  • Define evaluation frameworks for response quality metrics, retrieval-augmented generation benchmarking, and human-in-the-loop validation.
  • Partner with data engineering on pipelines, data quality, and governance; with MLOps on deployment, continuous integration and delivery, and monitoring; and with business and product teams on use-case alignment.
  • Own end-to-end delivery across multiple projects.
  • Mentor and guide junior engineers and project teams.
  • Conduct technical reviews, solution walkthroughs, and code reviews.
  • Support presales, requests for proposals, and solution proposals with architecture input.
  • Drive reusable accelerators, frameworks, and center-of-excellence assets.
  • Stay current with industry developments and help shape EXL’s generative AI strategy.
  • Influence technology choices, design decisions, and roadmap planning.

Desired Qualifications

  • Experience with fine-tuning approaches such as LoRA, PEFT, or prompt tuning.
  • Experience with the Azure AI stack, including Azure OpenAI and AI Search.
  • Exposure to enterprise security and data privacy in generative AI.
  • Exposure to coding agents or autonomous agent frameworks.
  • Experience in insurance or banking, financial services, and insurance domains.

About the Company

EXL

Analyzes business data and runs outsourced operations

EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1999

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