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EXL

Analyzes business data and runs outsourced operations

Lead AI Data Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNoida, Uttar Pradesh, IndiaNo salary listedIn-person

About the Job

Requirements

  • Have 9–12 years of total experience.
  • Have 2–4 or more years of hands-on experience delivering production LLM or generative AI use cases.
  • Have 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.
  • Have experience with LangChain, LangGraph, or similar frameworks, as well as agent orchestration and tool-calling architectures.
  • Have a deep understanding of LLM limitations, evaluation, and optimization strategies.
  • Have strong Python and PySpark engineering expertise for production-grade development, with proven API integration experience.
  • Have deep data analysis experience and experience handling large volumes of data.
  • Have data engineering integration skills with Fabric, Azure Databricks, or Snowflake.
  • Have exposure to cloud platforms such as Azure, AWS, or GCP; SQL; containers; CI/CD; and monitoring.
  • Have 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.
  • Have experience leading solution design or small teams.
  • Be able to translate business problems into AI solutions.
  • Have experience with fine-tuning approaches such as LoRA, PEFT, or prompt tuning.
  • Have experience with the Azure AI stack, including Azure OpenAI or AI Search.
  • Have exposure to enterprise security and data privacy in generative AI, and to coding agents or autonomous agent frameworks.
  • Have experience in insurance or BFSI domains is valuable for the employer's use cases.

Responsibilities

  • Architect enterprise-grade agentic and LLM 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 LLMs for knowledge assistants, document automation and intelligence, and workflow orchestration.
  • Implement 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, from data ingestion and chunking through 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, including with FastAPI or Flask.
  • Drive code quality, testing standards, and reusable architecture components.
  • Ensure solutions are optimized for performance, latency, cost, and reliability.
  • Implement LLM guardrails for hallucination control, safety filtering, and policy enforcement.
  • Define evaluation frameworks covering response-quality metrics, RAG benchmarking, and human-in-the-loop validation.
  • Partner with data engineering on pipelines, data quality, and governance; with MLOps on deployment, CI/CD, 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 pre-sales, RFPs, and solution proposals with architecture input.
  • Drive reusable accelerators, frameworks, and center-of-excellence assets.
  • Stay current with industry developments and help shape the employer'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 or AI Search.
  • Exposure to enterprise security and data privacy in generative AI, and to coding agents or autonomous agent frameworks.
  • Experience in insurance or BFSI domains is valuable for the employer's use cases.

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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