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EXL

Architect, AI Data Engineer

EXL

Architect AI Data Engineer with extensive experience in GenAI systems. Leading architecture, design, and enterprise-scale deployment of LLM-powered systems.

Posted 7/4/2026full-timeGurugram • 🇮🇳 IndiaJuniorWebsite

Core Competencies

Role fit
Core Competencies

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

Demonstrates expertise in defining and leading architecture for enterprise GenAI platforms, focusing on scalable agentic systems, RAG pipelines, and compliance with data security standards. Proven ability to optimize solutions for performance and reliability while driving the adoption of Responsible AI practices.

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 SystemsLLMsRAG PipelinesPythonPysparkSQLData AnalysisAgent OrchestrationFunction OrchestrationEvaluation Frameworks
Tools & Technologies
FastAPIFlaskLangChainLangGraphAzure DatabricksSnowflakeDockerKubernetesCI/CD PipelinesMonitoring & Observability
Industry Keywords
Enterprise SolutionsData SecurityResponsible AIOrchestration FrameworksPrompt Architecture

Tech Stack

Tools & technologies
AWSAzureCloudDockerFlaskGoogle 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)
  • Optimize 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)

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
  • Familiarity with Containers (Docker/Kubernetes)
  • CI/CD pipelines
  • Monitoring & observability

Benefits

Comp & perks
  • Provide technical leadership and mentorship to engineering teams
  • Act as a solution advisor to clients/stakeholders (including pre-sales, PoCs, solutioning)
  • Drive COE initiatives, knowledge sharing, and internal capability building
  • Health insurance
  • 401(k) matching
  • Flexible work hours
  • Paid time off
  • Remote work options