Architect AI Data Engineer

EXL; Maharashtra; On-site; INR 3,000,000 - 4,200,000; Full time

Job summary

EXL is seeking a seasoned GenAI Architect to define and drive end-to-end enterprise GenAI platforms. You will design scalable agentic systems, orchestration frameworks, and robust RAG pipelines while guiding tool-calling workflows and memory strategies.

You will lead architecture decisions on LLM integration, data pipelines, and cloud platform alignment, partnering with data engineering and MLOps teams to ensure scalable, secure, and compliant solutions across domains.

Qualifications

• 12–15 years total experience with 3+ years GenAI/LLM-based systems.
• Proven experience leading architecture and delivery of enterprise solutions.
• Hands-on with LLMs and RAG pipelines; strong architectural judgement.
• Experience with LangChain, LangGraph, and tool orchestration.
• Strong Python/PySpark engineering and API integration.

Responsibilities

• Define and lead end-to-end architecture for GenAI platforms.
• Design scalable agentic systems and memory strategies.
• Lead RAG pipelines with retrieval accuracy and evaluation.
• Collaborate with MLOps on CI/CD, deployment pipelines, monitoring.
• Provide technical leadership and mentor engineering teams.

Skills

GenAI architecture

LLM systems

LangChain

LangGraph

Python

PySpark

Cloud platforms

Data pipelines

Tools

Docker

Kubernetes

Azure Databricks

Snowflake

OpenAI API

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)