Architect AI Data Engineer

EXL; Gurugram District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

EXL is seeking a senior architecture leader for enterprise GenAI platforms. You will define end-to-end architecture, design scalable agentic systems, and drive architecture decisions across RAG, fine-tuning, and hybrid approaches.

You will collaborate with MLOps, data engineering, and business stakeholders to deliver robust, scalable GenAI solutions. You will mentor engineering teams, present to CXOs, and drive COE initiatives, knowledge sharing, and capability building.

Qualifications

• 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.).

Responsibilities

• Define and lead end-to-end architecture for GenAI platforms.
• Design scalable agentic systems and orchestration frameworks.
• Lead architecture decisions on RAG/fine-tuning/hybrid approaches.
• Oversee CI/CD, deployment pipelines, and monitoring with MLOps teams.
• Partner with data engineering, governance, and security teams to ensure compliance.

Skills

GenAI architecture

Architecture leadership

Python

Cloud platforms

Data engineering

LLMs/Large Language Models

MLOps collaboration

API design

LangChain/LangGraph

Tools

LangChain

LangGraph

FastAPI

Flask

Azure/AWS/GCP

Job description

• 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
• Agentic AI & LLM Engineering Leadership
• 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
• Optimise solutions for latency, cost, scalability, and reliability
• Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
• 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)
• 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
• Metadata management and knowledge graph strategies
• Work with business stakeholders to: - Identify high-value GenAI use cases
• Translate business problems into AI-driven solutions
• Leadership & Stakeholder Management
• Provide technical leadership and mentorship to engineering teams
• Act as a solution advisor to clients/stakeholders (including pre-sales, PoCs, solutioning)
• Present architecture and design decisions to senior leadership and CXOs
• Drive COE initiatives, knowledge sharing, and internal capability building

Key Responsibilities

• 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
• Agentic AI & LLM Engineering Leadership
• 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
• Optimise solutions for latency, cost, scalability, and reliability
• Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
• 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)
• 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
• Metadata management and knowledge graph strategies
• Work with business stakeholders to: - Identify high-value GenAI use cases
• Translate business problems into AI-driven solutions
• Leadership & Stakeholder Management
• Provide technical leadership and mentorship to engineering teams
• Act as a solution advisor to clients/stakeholders (including pre-sales, PoCs, solutioning)
• Present architecture and design decisions to senior leadership and CXOs
• Drive COE initiatives, knowledge sharing, and internal capability building

Experience

Must-Have Skills & Experience

• 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
• Proven experience in leading architecture and delivery of enterprise solutions

LLM / GenAI & Agentic Engineering

• 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
• Deep understanding of: - LLM limitations, evaluation, and optimisation strategies

Core Engineering

• 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

Cloud & Platform

• Hands-on experience with Azure / AWS / GCP
• Familiarity with: - Containers (Docker/Kubernetes)
• CI/CD pipelines
• Monitoring & observability

Data / AI Foundations (Mandatory)

Prior Experience In One Or More

• Data Engineering (ETL/ELT, pipelines, orchestration)
• Data Science / ML lifecycle (especially NLP)
• Analytics engineering / data products

Good-to-Have / Preferred

• Fine-tuning techniques (LoRA, PEFT, prompt tuning)
• Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
• Knowledge of knowledge graphs, semantic layers, or enterprise search
• Experience in domain-specific GenAI solutions (Insurance, BFSI, Healthcare)