AI Consulting – Deep Tech Engineering Lead 

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. 

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. 

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 function calling, RAG, and at least one production Agentic AI system. 
• Cloud AI platform: deep experience with one of Azure, OpenAI, AWS Bedrock, Claude; Kubernetes and cloud-native deployment. 
• LLMOps & evaluation: CI/CD for AI, automated evals, experiment tracking, observability, model lifecycle management. 
• Responsible AI: governance frameworks, guardrails, model safety, compliance, and auditability. 

Depth in several of the following (we do not expect all) 

• Orchestration frameworks: LangChain / LangGraph, LlamaIndex, CrewAI, AutoGen, DSPy, Semantic Kernel, Strands. 
• Advanced retrieval: Graph RAG, knowledge graphs, context graphs, hybrid search, vector databases. 
• Agentic patterns: multi-agent architectures, planning and reasoning, MCP (Model Context Protocol). 
• Model customization: fine-tuning, distillation, and model evaluation at scale. 
• Frontier model families: OpenAI, Claude, Gemini, Llama, Mistral. 

Qualifications 

• 10–15 years across software engineering, AI/ML, data science, or enterprise architecture, with a clear trajectory into senior technical leadership. 
• Proven track record architecting and deploying production-scale AI, from strategy through implementation. 
• Experience in a consulting, services, or client-facing delivery environment, including supporting pre-sales or solution shaping. 
• Experience leading globally distributed, multi-disciplinary teams. 
• Bachelor’s in Computer Science, AI, Engineering, Data Science, or a related field. Master’s preferred. 

What success looks like 

In the first 12 months 

• Multiple enterprise GenAI and Agentic solutions delivered into production to deliver value / outcomes. 
• Reference architectures and reusable accelerators adopted across programs, measurably reducing delivery time or cost. 
• AI governance, evaluation, and observability standards operational on live engagements. 

Ongoing 

• Recognized as the primary technical authority for complex client AI initiatives and executive relationships. 
• Technical leadership converts into won and expanded engagements and a growing qualified pipeline. 
• A stronger, higher-performing AI engineering team, with you still close to the technology. 

 The typical base pay range for this role across the U.S. is USD $240,500 - $277,000 per year.

The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits