10 hours ago
Noida, IndiaSenior
Responsibilities
- Lead the architecture and implementation of multi-agent AI systems and drive technical strategy for GenAI initiatives.
- Own end-to-end delivery of complex, production-scale AI systems while collaborating with stakeholders to define requirements.
- Build and maintain high-performance REST and WebSocket APIs using FastAPI and Pydantic.
- Implement agentic AI systems and production workflows using LangGraph, Deep Agents, AutoGen, and LangChain.
- Architect real-time, event-driven microservices using messaging platforms such as Apache Kafka.
- Integrate and optimize SQL, NoSQL, and vector databases including Postgres, MongoDB, ChromaDB, and Pinecone.
- Operate production agent workflows with checkpointing, persistence, human-in-the-loop controls, observability, and evaluation tooling.
- Apply LLM safety guardrails for prompt-injection mitigation, PII handling, and content moderation in regulated industries.
- Mentor junior and mid-level engineers and provide technical guidance across teams.
Requirements
- Bachelor's degree in Computer Science, Data Science, or a related field.
- At least 5 years of total professional experience, including 3+ years of hands-on software engineering experience and 2+ years working with GenAI and LLMs.
- Strong proficiency in Python, including Python 3.11+, async/await, type hints, modern patterns, and strict type checking.
- Experience architecting and delivering end-to-end GenAI solutions, multi-agent systems, production ML/AI products, and prompt-engineering solutions.
- Strong software engineering expertise in object-oriented programming, SOLID principles, testing, code review, documentation, scalability, and maintainability.
- Experience building production REST/WebSocket APIs and microservices with FastAPI and related technologies.
- Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, or similar.
- Experience with message streaming platforms such as Kafka, Pulsar, or similar.
- Strong knowledge of SQL, NoSQL, and vector databases.
- Working knowledge of RAG pipelines, including embeddings, chunking, and retrieval, plus LLM observability and evaluation tools such as LangSmith or Langfuse.
- Ability to diagnose and resolve production failures, explain complex technical concepts to non-technical stakeholders, and provide technical leadership and mentoring.
Tech Stack
Categories
About EXL
EXL provides data analytics, AI, and digital operations services for enterprises in insurance, healthcare, banking, and other regulated industries. It delivers consulting, managed services, and technology platforms to improve decision-making, risk management, and operational efficiency. Founded in 1999 and headquartered in New York, EXL is a public company listed on the NASDAQ (ticker: EXLS).