AI Consulting – Deep Tech Engineering Lead (Senior AVP/VP)

Role : AI Consulting – Deep Tech Engineering Lead (VP/Sr. AVP)

Band : VP / Senior AVP

Location : NY/ NJ (preferred) / Remote

Hire Type : Full-Time with EXL

Senior Consultant who turns client AI ambition into production systems that ship, scale, and win follow-on work. Equal parts architect, hands-on engineer, and trusted advisor to client executives.

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.

Key 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 technical problems.

Design APIs, microservices, and cloud-native AI services that underpin enterprise AI ecosystems.

Guide teams on software architecture, performance, scalability, security, and maintainability.

AI governance & LLMOps

Architect governance frameworks for auditability, observability, explainability, and compliance.

Design guardrails for hallucination, prompt injection, toxicity, and model safety.

Establish LLMOps: evaluation pipelines, automated testing, CI/CD, monitoring, and production governance.

Define evaluation frameworks spanning quality, safety, reliability, latency, and business outcomes.

Required technical skills

Core (depth expected across all of these)

Programming & engineering: expert Python; REST APIs and microservices; FastAPI; distributed systems; cloud-native architecture.

LLM application delivery: prompt engineering, tool and f…