AI Developer

About EXL

 EXL is recognized as a global leader in analytics, artificial intelligence, and digital solutions, supporting a wide range of industries through innovative technology and data-driven strategies. Listed on NASDAQ under the ticker EXLS, EXL has a market valuation of approximately $1.3 billion and a global workforce of 48,000+ employees. With a strong presence across continents and a portfolio of 800+ clients worldwide, EXL continues to drive digital transformation at scale through cutting-edge engineering, domain expertise, and customer-centric solutions.

Description for Candidates - We are looking for an Agentic AI Engineer to design, build, execute, test, and orchestrate autonomous AI agent systems that operate across complex, multi-step workflows. You will work at the intersection of large language models, tool-use frameworks, and enterprise data pipelines to deliver reliable, production-grade agentic solutions.

Responsibilities forCandidates - 

● Design and implement agentic AI systems (single and multi-agent) with tool use, memory, and fallback mechanisms.

 ● Build production-grade agents using frameworks like LangGraph, AutoGen, CrewAI, or custom LLM orchestration layers.

 ● Implement agent reasoning loops including planning, tool selection, execution, observation, and re-planning with safety guardrails.

 ● Develop prompt and context engineering strategies for reliable, grounded LLM outputs. 

● Design agent orchestration workflows include task routing, parallel execution, state management, retries, and human-in-the-loop escalation.

 ● Build evaluation frameworks for LLMs and agents including automated testing, adversarial testing, and performance benchmarking. 

● Implement retrieval and grounding using vector databases, embeddings, and knowledge graphs for contextual accuracy. 

● Ensure observability of agent systems by tracing LLM calls, tool usage, and decision paths using monitoring tools. 

● Apply security and governance controls including prompt injection defense, access control, and safe tool execution. 

● Optimize agent systems for latency, cost, and scalability in production environments. 

● Build CI/CD pipelines for agent workflows including versioning, testing, and controlled deployments. 

● Integrate agents with enterprise systems and APIs to automate end-to-end business workflows.

 ● Design feedback loops using production traces and evaluation signals to continuously improve agent performance.

 ● Experience with Model Context Protocol (MCP) systems to design database connections, integrate APIs, and enable secure tool orchestration for AI agents.

 ● Hands-on experience in fine-tuning LLMs for domain-specific applications using LoRA, PEFT, QLoRA, RLHF, instruction tuning, and other parameter-efficient adaptation techniques. 

● Stay current with emerging agentic AI frameworks, research, and best practices for production deployment.

Qualifications for  Candidates - 
● Minimum 2 years of AI engineering experience, with at least 1 year focused on LLM/agent systems in production.

 ● Hands-on experience designing agentic architectures: ReAct, plan-and-execute, reflection loops, tool-use patterns.

 ● Proficiency in Python; experience with at least one agent framework (LangChain/LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent). 

● Strong understanding of prompt engineering, context window management, and structured output extraction. 

● Experience building and testing tool-use integrations: REST APIs, code interpreters, vector databases, SQL executors.

 ● Familiarity with evaluation frameworks for LLM outputs (RAGAS, custom eval harnesses, LLM-as-judge patterns).

 ● Understanding of agent safety concerns: prompt injection, tool misuse, hallucination detection, and mitigation strategies.

 ● Experience with cloud infrastructure (AWS/GCP/Azure) and containerization (Docker, Kubernetes). 

● Experience with MLOps, AIOps tooling (MLflow, Weights & Biases, experiment tracking). Strong experience designing and building memory and caching layers for agentic AI systems, including conversational memory, semantic retrieval, context optimization, and token cost reduction strategies for scalable production deployments