Architect, AI Data Engineer

EXL; Dadri; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

EXL is seeking an experienced AI Engineer to lead the architecture and strategy for enterprise GenAI platforms. The ideal candidate has over 12 years of experience, including significant work in GenAI and LLM systems. Responsibilities include designing scalable systems, enforcing governance standards, and collaborating with engineering teams.

A strong background in Python, cloud platforms, and data engineering is essential. The candidate will drive best practices and ensure integration with enterprise data solutions.

Qualifications

• 12–15 years of total experience with 3+ years in GenAI/LLM systems.
• Proven record in leading architecture for enterprise solutions.
• Strong hands-on experience with LLMs like Claude and OpenAI.

Responsibilities

• Lead architecture for enterprise GenAI platforms and use cases.
• Design and implement agentic workflows.
• Ensure compliance with data security and privacy standards.

Skills

Leadership

Architecture Design

GenAI Solutions

Python/Pyspark

Cloud Platforms

Tools

Azure

AWS

GCP

Docker

Kubernetes

Job description

Key Responsibilities

1. Solution Architecture & Strategy

• 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

2. 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
• Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
• Optimise solutions for latency, cost, scalability, and reliability

3. Platform & Engineering Excellence

• 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

4. Governance, Risk & Responsible AI

• 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)

5. Data & Ecosystem Collaboration

• 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

6. 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

Must-Have Skills & Experience

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)