AI Architect

EXL; Dadri, Pune District, Bengaluru; Hybrid; INR 4,000,000 - 6,000,000; Full time

Job summary

EXL is seeking an Applied Agentic AI Architect to design, build, and productionize autonomous and semi-autonomous AI agent systems that power real business outcomes.

This hands-on role covers end-to-end architecture, including multi-agent orchestration, tool-calling, memory, retrieval, and guardrails, with deployment at scale across industries such as insurance, healthcare, banking, and customer operations.

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.
• 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 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.

Responsibilities

• Architect agentic systems with multi-agent orchestration, tool-calling, memory, and retrieval in production.
• Build with modern agent frameworks using LangGraph and ADK.
• Design enterprise conversational and voice AI experiences including CX platforms such as GECX.
• Evaluate and integrate frontier LLMs (Gemini, Grok) with balanced trade-offs on accuracy and cost.
• Design RAG pipelines, grounding strategies, and retrieval-enhanced solutions.
• Establish evaluation harnesses, guardrails, and monitoring for agent behavior in production.
• Lead productionization, CI/CD for prompts and agents, and cost optimization.
• Mentor engineers and data scientists, set reusable architecture patterns and patterns.

Tools

LangGraph

ADK (Agent Development Kit)

GECX

Gemini

Grok

LangChain

LlamaIndex

Semantic Kernel

Job description

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.