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EXL

Architect, AI Data Engineer

EXL

Architect AI Data Engineer designing enterprise GenAI platforms and systems. Leading architecture, strategy, and engineering excellence for generative AI initiatives across business domains.

Posted 7/4/2026full-timeNoida • 🇮🇳 IndiaSeniorLeadWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive expertise in defining and leading architecture for enterprise GenAI platforms, with a strong focus on LLMs, RAG pipelines, and scalable agentic systems. Proven ability to implement best practices in engineering, data integration, and compliance with data security standards.

Highest-signal resume keywords
GenAI Architecture LeadershipLLM Implementation ExperienceRAG Pipeline DevelopmentPython/Pyspark Engineering ExpertiseCloud Platform Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
GenAI PlatformsLLMsRAG PipelinesPythonPysparkSQLData EngineeringNLPETL/ELTData Analysis
Tools & Technologies
FastAPIFlaskLangChainLangGraphAzure DatabricksSnowflakeDockerKubernetesCI/CDMonitoring
Industry Keywords
Enterprise SolutionsData SecurityResponsible AIOrchestration FrameworksData Governance

Tech Stack

Tools & technologies
AWSAzureCloudDockerETLFlaskGoogle Cloud PlatformKubernetesMicroservicesPySparkPythonSQL

About the role

Key responsibilities & impact
  • Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
  • Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
  • Establish reference architectures, design patterns, and reusable frameworks
  • Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
  • Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
  • Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
  • Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
  • Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
  • Optimise solutions for latency, cost, scalability, and reliability
  • Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
  • Define engineering best practices: coding standards, testing, packaging, observability
  • Ensure seamless integration with enterprise data platforms, APIs, and business applications
  • Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
  • Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
  • Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
  • Ensure compliance with data security, privacy, and enterprise governance standards
  • Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
  • Partner with Data Engineering teams on Data ingestion, pipelines, and quality controls

Requirements

What you’ll need
  • 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
  • Proven experience in leading architecture and delivery of enterprise solutions
  • Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
  • RAG pipelines and retrieval optimisation
  • GPT + Agentic AI implementation experience
  • Experience with LangChain, LangGraph, or similar frameworks
  • Agent orchestration and tool-calling architectures
  • Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
  • Deep data analysis experience and handling large volume of data
  • Fabric/Azure Databricks/Snowflake data engineering integration skills
  • Good exposure to Cloud platforms (Azure/AWS/GCP)
  • SQL
  • Containers, CI/CD, monitoring
  • Hands-on experience with Azure / AWS / GCP
  • Familiarity with Containers (Docker/Kubernetes)
  • CI/CD pipelines
  • Monitoring & observability
  • Prior experience in Data Engineering (ETL/ELT, pipelines, orchestration) or Data Science / ML lifecycle (especially NLP) or Analytics engineering / data products

Benefits

Comp & perks
  • Health insurance
  • Retirement plans
  • Paid time off
  • Flexible work arrangements
  • Professional development