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EXL

Analyzes business data and runs outsourced operations

Architect 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 12 years of total experience, including 3 or more years working with generative AI or large language model-based systems.
  • Proven experience leading architecture and delivery of enterprise solutions.
  • Hands-on experience with large language models such as Claude or OpenAI, retrieval-augmented generation pipelines and retrieval optimization, 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 optimization strategies.
  • Strong Python and PySpark engineering expertise in production-grade development, with proven API integration experience.
  • Deep data analysis experience and experience handling large volumes of data.
  • Data engineering integration skills with Fabric, Azure Databricks, or Snowflake.
  • Exposure to cloud platforms such as Azure, AWS, or GCP; SQL; containers; CI/CD; and monitoring.
  • Hands-on experience with Azure, AWS, or GCP.
  • Familiarity with Docker or Kubernetes containers, CI/CD pipelines, and monitoring and observability.
  • 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/data products.

Responsibilities

  • Define and lead end-to-end architecture for enterprise generative AI platforms and use cases.
  • Design scalable single-agent and multi-agent systems and orchestration frameworks.
  • Establish reference architectures, design patterns, and reusable frameworks.
  • Lead architecture decisions on retrieval-augmented generation, fine-tuning, or hybrid approaches.
  • Evaluate technologies such as large language models, vector databases, and orchestration frameworks, and recommend best-fit solutions.
  • Design and implement complex agentic workflows using tool calling, function orchestration, and memory strategies.
  • Build enterprise-grade retrieval-augmented generation pipelines focused on retrieval accuracy and evaluation.
  • Drive prompt architecture standards, including prompt libraries, chaining, and orchestration governance.
  • Optimize solutions for latency, cost, scalability, and reliability.
  • Lead development of generative AI platforms, APIs, and microservices using technologies such as FastAPI and Flask.
  • Define engineering best practices for coding standards, testing, packaging, and observability.
  • Ensure integration with enterprise data platforms, APIs, and business applications.
  • Collaborate with MLOps teams on CI/CD, deployment pipelines, versioning, and monitoring.
  • Define and enforce large language model guardrails, including hallucination control, safety filters, and policy enforcement.
  • Implement evaluation frameworks for retrieval-augmented generation evaluation, prompt testing, and benchmarking.
  • Ensure compliance with data security, privacy, and enterprise governance standards.
  • Drive responsible AI practices, including bias mitigation, explainability, and auditability.
  • Partner with data engineering teams on data ingestion, pipelines, quality controls, metadata management, and knowledge graph strategies.
  • Work with business stakeholders to identify high-value generative AI use cases and translate business problems into AI-driven solutions.
  • Provide technical leadership and mentorship to engineering teams.
  • Advise clients and stakeholders on solutions, including pre-sales, proofs of concept, and solutioning.
  • Present architecture and design decisions to senior leadership and C-suite executives.
  • Drive center of excellence initiatives, knowledge sharing, and internal capability building.

Desired Qualifications

  • Experience with fine-tuning techniques such as LoRA, PEFT, and prompt tuning.
  • Experience with the Azure AI stack, including Azure OpenAI and Cognitive Search.
  • Knowledge of knowledge graphs, semantic layers, or enterprise search.
  • Experience delivering domain-specific generative AI solutions in insurance, banking and financial services, or healthcare.

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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