Senior AI Architect

About the Role

EXL is looking for an Applied Agentic AI Architect to design, build, and productionize autonomous and semi-autonomous AI agent systems that power real business outcomes for our clients. This is a hands-on architecture role: you will own the technical design of multi-agent and conversational AI solutions end to end — from orchestration patterns and model selection to evaluation, guardrails, and deployment at scale.

You will sit at the intersection of applied research and engineering, translating fast-moving agentic AI capabilities into robust, secure, and cost-effective solutions across domains such as insurance, healthcare, banking, and customer operations. You will work directly with client stakeholders, data scientists, and engineering teams to move solutions from proof-of-concept to enterprise production.

Key Responsibilities

• Architect agentic systems. Design multi-agent architectures — planner/executor patterns, tool-calling, memory, retrieval, and human-in-the-loop workflows — that are reliable, observable, and maintainable in production.
• Build with modern agent frameworks. Implement solutions using frameworks such as LangGraph and Google's Agent Development Kit (ADK), selecting the right orchestration pattern for each use case.
• Design conversational AI. Architect enterprise conversational and voice AI experiences — including contact-center and customer-experience solutions on platforms such as GECX — covering intent handling, dialog management, escalation, and containment.
• Model strategy & selection. Evaluate and integrate frontier LLMs including Gemini, Grok, and others; make principled trade-offs across accuracy, latency, context window, cost, and deployment constraints.
• Retrieval & grounding. Design RAG pipelines, knowledge integration, and grounding strategies to reduce hallucination and improve factual reliability.
• Evaluation & guardrails. Establish evaluation harnesses, offline/online metrics, red-teaming, safety guardrails, and monitoring for agent behavior in production.
• Productionize & scale. Own MLOps/LLMOps concerns — CI/CD for prompts and agents, versioning, observability, caching, and cost optimization.
• Lead technically. Set architecture standards and reusable patterns, review designs, mentor engineers and data scientists, and act as a trusted technical advisor to clients.
• Stay ahead. Track the rapidly evolving agentic AI landscape and translate new techniques into practical, differentiated client offerings.

Required Qualifications

• Bachelor's or Master's in Computer Science, AI/ML, Engineering, or equivalent practical experience.
• 8+ years in software/AI engineering, with 3+ years building and shipping production ML/GenAI systems (architecture-level experience strongly preferred).
• Hands-on experience designing and deploying agentic AI systems — multi-agent orchestration, tool use, memory, and planning.
• Practical experience with agent frameworks such as LangGraph and ADK (Agent Development Kit).
• Demonstrated experience building Conversational AI / voice AI solutions, ideally including CX/contact-center platforms such as GECX.
• Working experience with frontier LLMs such as Gemini, Grok, and comparable models, including prompt engineering and evaluation.
• Strong programming skills in Python and solid software engineering fundamentals (APIs, testing, version control, system design).
• Experience with RAG, vector stores, and embedding-based retrieval.
• Familiarity with at least one major cloud (GCP, Azure, or AWS) and modern MLOps/LLMOps practices.

Preferred Qualifications

• Experience deploying agentic solutions in regulated industries (insurance, healthcare, banking, financial services).
• Knowledge of AI safety, responsible AI, security, and data privacy in enterprise settings.
• Experience with observability/eval tooling for LLM applications and agent tracing.
• Exposure to additional frameworks and tooling (e.g., LangChain, LlamaIndex, Semantic Kernel, MCP-based tool integration).
• Prior client-facing or consulting experience, with strong communication and stakeholder-management skills.
• Contributions to open-source AI projects, publications, or a track record of applied AI innovation.

What Success Looks Like

• Agentic and conversational AI solutions move reliably from concept to production, with measurable business impact.
• Reusable architecture patterns, evaluation frameworks, and guardrails become standard across engagements.
• Clients trust you as the go-to authority on applied agentic AI.

What We Offer

• The opportunity to build cutting-edge agentic AI systems that reach millions of users.
• A collaborative, research-informed engineering culture.
• Competitive compensation, benefits, and growth opportunities.

About the Role

EXL is looking for an Applied Agentic AI Architect to design, build, and productionize autonomous and semi-autonomous AI agent systems that power real business outcomes for our clients. This is a hands-on architecture role: you will own the technical design of multi-agent and conversational AI solutions end to end — from orchestration patterns and model selection to evaluation, guardrails, and deployment at scale.

You will sit at the intersection of applied research and engineering, translating fast-moving agentic AI capabilities into robust, secure, and cost-effective solutions across domains such as insurance, healthcare, banking, and customer operations. You will work directly with client stakeholders, data scientists, and engineering teams to move solutions from proof-of-concept to enterprise production.

Key Responsibilities

• Architect agentic systems. Design multi-agent architectures — planner/executor patterns, tool-calling, memory, retrieval, and human-in-the-loop workflows — that are reliable, observable, and maintainable in production.
• Build with modern agent frameworks. Implement solutions using frameworks such as LangGraph and Google's Agent Development Kit (ADK), selecting the right orchestration pattern for each use case.
• Design conversational AI. Architect enterprise conversational and voice AI experiences — including contact-center and customer-experience solutions on platforms such as GECX — covering intent handling, dialog management, escalation, and containment.
• Model strategy & selection. Evaluate and integrate frontier LLMs including Gemini, Grok, and others; make principled trade-offs across accuracy, latency, context window, cost, and deployment constraints.
• Retrieval & grounding. Design RAG pipelines, knowledge integration, and grounding strategies to reduce hallucination and improve factual reliability.
• Evaluation & guardrails. Establish evaluation harnesses, offline/online metrics, red-teaming, safety guardrails, and monitoring for agent behavior in production.
• Productionize & scale. Own MLOps/LLMOps concerns — CI/CD for prompts and agents, versioning, observability, caching, and cost optimization.
• Lead technically. Set architecture standards and reusable patterns, review designs, mentor engineers and data scientists, and act as a trusted technical advisor to clients.
• Stay ahead. Track the rapidly evolving agentic AI landscape and translate new techniques into practical, differentiated client offerings.

Required Qualifications

• Bachelor's or Master's in Computer Science, AI/ML, Engineering, or equivalent practical experience.
• 8+ years in software/AI engineering, with 3+ years building and shipping production ML/GenAI systems (architecture-level experience strongly preferred).
• Hands-on experience designing and deploying agentic AI systems — multi-agent orchestration, tool use, memory, and planning.
• Practical experience with agent frameworks such as LangGraph and ADK (Agent Development Kit).
• Demonstrated experience building Conversational AI / voice AI solutions, ideally including CX/contact-center platforms such as GECX.
• Working experience with frontier LLMs such as Gemini, Grok, and comparable models, including prompt engineering and evaluation.
• Strong programming skills in Python and solid software engineering fundamentals (APIs, testing, version control, system design).
• Experience with RAG, vector stores, and embedding-based retrieval.
• Familiarity with at least one major cloud (GCP, Azure, or AWS) and modern MLOps/LLMOps practices.

Preferred Qualifications

• Experience deploying agentic solutions in regulated industries (insurance, healthcare, banking, financial services).
• Knowledge of AI safety, responsible AI, security, and data privacy in enterprise settings.
• Experience with observability/eval tooling for LLM applications and agent tracing.
• Exposure to additional frameworks and tooling (e.g., LangChain, LlamaIndex, Semantic Kernel, MCP-based tool integration).
• Prior client-facing or consulting experience, with strong communication and stakeholder-management skills.
• Contributions to open-source AI projects, publications, or a track record of applied AI innovation.

What Success Looks Like

• Agentic and conversational AI solutions move reliably from concept to production, with measurable business impact.
• Reusable architecture patterns, evaluation frameworks, and guardrails become standard across engagements.
• Clients trust you as the go-to authority on applied agentic AI.

What We Offer

• The opportunity to build cutting-edge agentic AI systems that reach millions of users.
• A collaborative, research-informed engineering culture.
• Competitive compensation, benefits, and growth opportunities.

Bachelor's/Master's