GenAI Solution Architect - Banking AI, Hybrid UK

Ex; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Private healthcare

Critical illness cover

Life insurance

Pension

Cycle scheme

Employee stock purchase plan

Job summary

EXL, a global data and AI company, is seeking a seasoned Data & AI Solution Architect to lead GenAI initiatives for enterprise clients, focusing on architecture, delivery, and governance. The role requires strong expertise in LLM ecosystems, RAG, and multi-agent AI, with collaboration across engineering, data, and business teams.

Hybrid work in the UK, with a clear path to production-scale GenAI deployments.

Qualifications

• 7+ years of technology/engineering experience.
• Minimum 3 years delivering Generative AI solutions in production.
• Experience architecting GenAI deployments in enterprise environments.
• Strong expertise with major LLM platforms (OpenAI, Anthropic, Gemini).
• Hands-on with orchestration tools (LangChain, LangGraph, AutoGen, CrewAI).
• Cloud experience across Azure, AWS, or GCP.
• Excellent stakeholder management and communication skills.

Responsibilities

• Lead end-to-end architecture, design, and delivery of GenAI solutions for banking clients.
• Translate business needs into scalable, production-ready AI architectures.
• Design and implement GenAI solutions (RAG, multi-agent systems, knowledge platforms).
• Define architecture governance, security controls, and best practices.
• Collaborate with engineering, data, cloud, and business teams for delivery.
• Drive solution assurance across SDLC including reviews, testing, deployment, monitoring.

Skills

Generative AI

Architecture

LLM ecosystems

RAG architectures

LangChain

Cloud architecture

Stakeholder mgmt

DevOps/MLOps

Tools

LangGraph

AutoGen

CrewAI

Azure

AWS

GCP

Job description

EXL, a global data and AI company, is seeking a seasoned Data & AI Solution Architect to lead GenAI initiatives for enterprise clients, focusing on architecture, delivery, and governance. The role requires strong expertise in LLM ecosystems, RAG, and multi-agent AI, with collaboration across engineering, data, and business teams.

Hybrid work in the UK, with a clear path to production-scale GenAI deployments.