Gen AI Engineer/ Developer/ Architect

EXL (NASDAQ: EXLS) is a $7 billion public-listed NASDAQ company and a rapidly expanding global digital data-led AI transformation solutions company with double digit growth. EXL Digital division spearheads the development and implementation of Generative AI (GenAI) business solutions for our clients in Banking & Finance, Insurance, and Healthcare. As a global leader in analytics, digital transformation, and AI innovation, EXL is committed to helping clients unlock the potential of generative AI to drive growth, efficiency, and innovation. Job Summary We are seeking a visionary Generative AI Architect with strong hands-on experience to lead the design and deployment of intelligent solutions leveraging Large Language Models (LLMs), agentic frameworks, and advanced NLP techniques. This role emphasizes deep expertise in LangChain, LangGraph, agentic workflows to build scalable, enterprise-grade generative AI systems across insurance, healthcare, and financial services. Key Responsibilities: Architect modular GenAI solutions using LLMs (e.g., GPT, Claude, LLaMA, Mistral) with a focus on LangChain , LangGraph and RAG. Design and implement agent-based systems capable of reasoning, tool use, API integration, and memory management using LangChain agents and LangGraph-based workflows. Design robust, cloud-native AI platforms capable of handling large-scale data pipelines and complex machine learning models (AWS/Azure/GCP) Develop and prototype intelligent assistants, document automation systems, and NLP tools. Build robust RAG architectures with vector databases (e.g., FAISS, Pinecone) and embedding models for domain-specific search and document intelligence. Translate complex business problems into generative AI use cases in areas such as underwriting automation, claims triage, policy analysis, and customer support. Collaborate with cross-functional teams including data scientists, engineers, and domain SMEs to drive successful AI solution delivery. Continuously evaluate and benchmark LLMs, agent orchestration tools, and GenAI platforms (e.g., OpenAI, Hugging Face, LlamaIndex). Optimize performance and scalability for LLM-based systems deployed in production, ensuring low latency, secure data handling, and ethical AI compliance. Evangelize GenAI innovations internally and with clients via PoCs, demos, and technical workshops. Good to Have: Develop structured AI pipelines using the MCP framework to clearly define model invocation logic (Model), contextual understanding and memory (Context), and input/output protocols (Protocol) for repeatable, enterprise-scale use cases Qualifications: 7+ years of experience in AI/ML, including 3+ years specializing in NLP and Generative AI. Proven hands-on expertise with LLMs such as GPT, Claude, LLaMA, Mistral, and BERT, including both open-source and commercial APIs (e.g., OpenAI, Anthropic). Deep understanding of LLM architecture and operations, including prompt engineering, function calling, tool use, memory management, context handling, and token optimization. Strong experience building Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding models. Proficient with GenAI frameworks including LangChain, LangGraph, and LlamaIndex, with a solid grasp of agent orchestration and workflow design. Advanced programming skills in Python, with hands-on experience using PyTorch, Transformers, and the broader Hugging Face ecosystem. Demonstrated ability to design, prototype, and deploy enterprise-ready AI solutions in production environments.