VP - Sr. AI Consulting Architect & Client Partner

Job Description: AI Consulting – Deep Tech Engineering Lead

About EXL:

EXL, from our beginnings in business process services to becoming a global leader in data and AI, EXL brings 25+ years of proven expertise helping enterprises transform and redefining what’s possible for our clients.

We partner with leading companies in insurance, healthcare, banking and capital markets, retail, media and communications, and energy and infrastructure to reimagine business models, deliver measurable outcomes, and accelerate innovation. While nearly 70% of enterprise AI initiatives fail, EXL consistently delivers a 90% success rate by integrating deep industry knowledge, robust data and analytics capabilities, and cutting-edge AI implementation in client workflows—turning complexity into value at scale.

At EXL, innovation isn’t just a buzzword—it’s how we solve complex challenges and create lasting value. By integrating cutting-edge technologies such as Agentic AI into workflows and data, and cloud computing, we empower our clients to remain at the forefront of innovation. Whether it’s revolutionizing customer experiences, streamlining operations, or uncovering new revenue streams, innovation fuels everything we do.

About the Job:

Why this role exists

Enterprises are past GenAI experimentation. They now demand production systems with measurable ROI, governance, and scale, and most stall in the gap between an impressive pilot and a system that survives production. EXL lives in that gap. We are accountable for outcomes on our clients’ data, in their environment, against their SLAs. This role is the senior technical authority who makes that real. You convert ambitious, under-specified client mandates into secure, production-grade Generative and Agentic AI systems, and you turn each engagement into reusable IP that compounds EXL’s delivery leverage and pipeline.

What you will own

• The senior technical relationship with client executives, and the technical shaping of the deals that fund the work.
• End-to-end technical vision and delivery for enterprise GenAI and Agentic AI programs across a portfolio of clients focused on delivering value / outcomes to the clients.
• The technical standard for the practice: reference architectures, reusable accelerators, and the governance and evaluation bar every engagement is held to.

Responsibilities: Commercial & client growth

• Partner with sales and practice leaders to shape pursuits, solution complex deals, and influence SOWs, estimates, and delivery approach.
• Serve as the senior technical voice in client pursuits and executive conversations, converting technical credibility into won and expanded engagements.
• Build reusable accelerators, reference architectures, and IP that lower delivery cost and differentiate EXL in the market.
• Identify expansion opportunities within accounts and translate delivered outcomes into follow-on pipeline.

Leadership & stakeholder management

• Lead and grow global, multi-disciplinary AI teams, and raise the technical bar across the practice.
• Partner with business and technology leaders to define AI roadmaps and implementation strategy.
• Mentor architects and engineers, and build a bench of senior technical talent.
• Translate complex architectures for executive and non-technical audiences.

Architecture & solution leadership

• Own architecture, design, and implementation of enterprise-grade GenAI and Agentic AI solutions from concept to production.
• Define scalable reference architectures and design patterns for AI platforms, copilots, and intelligent agents.
• Establish architectural standards, reusable components, and best practices across delivery programs.
• Design secure, resilient, highly available AI systems at enterprise scale, across structured and unstructured data.

Generative & Agentic AI delivery

• Design and deliver LLM-powered applications: conversational AI, enterprise copilots, knowledge management, and workflow automation.
• Architect advanced retrieval using RAG, Agentic RAG, Graph RAG, knowledge-graph, and hybrid approaches.
• Design multi-agent systems with modern orchestration and reasoning architectures, including human-in-the-loop and autonomous frameworks.
• Define memory, context, planning, tool-use, and reasoning strategies for agentic systems.

Hands-on engineering

• Stay technical. Contribute to architecture, critical-path code, design reviews, and the hardest techni…