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EXL

Analyzes business data and runs outsourced operations

Principal Architect AI Data Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeGurugram, Haryana, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 15 years of total experience in Data Engineering, Data Science, or AI.
  • At least 3 years of hands-on experience delivering LLM or GenAI solutions at scale.
  • Proven experience in architecture, solution design, and enterprise delivery.
  • Strong hands-on experience with large language models such as Claude and OpenAI, retrieval-augmented generation pipelines and retrieval optimisation, and GPT and agentic AI implementation.
  • Experience with LangChain, LangGraph, or similar frameworks, as well as agent orchestration and tool-calling architectures.
  • Deep understanding of large language model limitations, evaluation, and optimisation 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.
  • Skills integrating Fabric, Azure Databricks, or Snowflake for data engineering.
  • Exposure to cloud platforms including Azure, AWS, or GCP; SQL; containers; CI/CD; 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 and data products.

Responsibilities

  • Lead the design of enterprise-grade generative AI and agentic architectures, including single-agent, multi-agent, and tool-driven systems.
  • Define reference architectures, reusable frameworks, and best practices for large language model applications across the organisation.
  • Architect and oversee end-to-end retrieval-augmented generation pipelines, covering data ingestion, chunking, embeddings, vector search, orchestration, and response synthesis.
  • Drive scalability, reliability, cost optimisation, and performance across generative AI platforms.
  • Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows.
  • Guide teams on tool-calling, function-calling, memory handling, and multi-agent system design.
  • Lead efforts to reduce hallucinations and establish guardrails, safety mechanisms, and output evaluation frameworks.
  • Architect production-grade APIs and services, including FastAPI, Flask, or enterprise microservices, for large language model solutions.
  • Define MLOps and LLMOps pipelines, including CI/CD, monitoring, observability, and evaluation.
  • Partner with Data Engineering teams to ensure data quality, lineage, governance, compliance, and seamless integration with enterprise data platforms.
  • Build and scale a Generative AI and Agentic AI Centre of Excellence.
  • Define standardised frameworks, accelerators, and reusable components to improve delivery velocity.
  • Drive organisation-wide adoption of generative AI best practices and tooling standards.
  • Engage with executives, business stakeholders, and clients to translate business problems into AI-led solutions.
  • Lead solutioning, pre-sales, RFP responses, and client workshops for generative AI opportunities.
  • Influence organisational AI strategy, roadmap, and investment decisions.
  • Establish enterprise generative AI governance frameworks covering responsible AI, security, privacy, ethical usage, and compliance.
  • Define policies for data access, redaction, model usage, auditability, and explainability.
  • Mentor and guide architects, engineers, and data scientists.
  • Drive technical upskilling, hiring strategy, and capability maturity.
  • Review solution designs and enforce architecture quality standards.

Desired Qualifications

  • Experience with fine-tuning techniques such as LoRA, PEFT, prompt tuning, and few-shot learning.
  • Experience with enterprise generative AI deployments involving security, privacy, and governance.
  • Experience with the Azure ecosystem, including Azure OpenAI, AI Search, and Fabric.
  • Exposure to industry use cases in insurance, banking and financial services, healthcare, or retail.

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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