EXL—Data Scientist

Job description

Description: Key Responsibilities Leadership & Strategy - Lead architectural design and implementation of multi-agent AI systems - Drive technical strategy for GenAI initiatives and recommend best practices - Mentor and provide technical guidance to junior and mid-level engineers - Collaborate with stakeholders to define requirements and deliver solutions - Own end-to-end delivery of complex, production-scale AI systems Technical Execution - Build and maintain high-performance REST/WebSocket APIs using FastAPI (Pydantic v2) - Implement and optimize agentic AI systems using frameworks like LangGraph, Deep Agents, AutoGen, and LangChain - Architect real-time, event-driven microservices using messaging queues like Apache Kafka - Design clean, testable, maintainable services using SOLID principles, Python async, and type hints - Integrate and optimize SQL, NoSQL, and vector databases (Postgres, MongoDB, ChromaDB, Pinecone) - Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls, backed by LLM observability and evaluation tooling (e.g., LangSmith, Langfuse) - Apply LLM safety guardrails (prompt-injection mitigation, PII handling, content moderation) in line with Banking, Insurance, and Healthcare compliance requirements - Stay current with emerging trends in GenAI, deep learning, and AI orchestration framework Skills and Competencies - Proven ability to architect and deliver end-to-end GenAI solutions and multi-agent systems - Strong software engineering discipline: testing (unit, integration, performance), code review, documentation - Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders - Strategic thinking and problem-solving with a focus on scalability and maintainability - Leadership capability: mentoring, technical guidance, and cross-functional collaboration Requirements: Minimum Qualifications - Bachelor's degree in Computer Science, Data Science, or related field - 5+ years of total professional experience, including: - 3+ years of hands-on Software Engineering experience in Python, FastAPI and relevant tech stack - 2+ years working specifically with GenAI and LLMs (GPT, Claude, LLaMA, etc.) - Track record of shipping production ML/AI products, with strong prompt-engineering skills, systems-level thinking, and the ability to diagnose and resolve production failures - Strong software engineering background with expertise in OOP and SOLID principles - Proficiency in Python 3.11+ (async/await, type hints, modern Python patterns, strict type checking) - Experience with agentic frameworks (LangChain, LangGraph, AutoGen, or similar) - Proven track record building production REST/WebSocket APIs and microservices - Experience with message streaming platforms (Kafka, Pulsar, or similar) - Strong knowledge of databases: SQL, NoSQL, and vector databases - Working knowledge of RAG pipelines (embeddings, chunking, retrieval) and LLM observability/evaluation tools (LangSmith, Langfuse, or similar) Responsibilities: Key Responsibilities Leadership & Strategy - Lead architectural design and implementation of multi-agent AI systems - Drive technical strategy for GenAI initiatives and recommend best practices - Mentor and provide technical guidance to junior and mid-level engineers - Collaborate with stakeholders to define requirements and deliver solutions - Own end-to-end delivery of complex, production-scale AI systems Technical Execution - Build and maintain high-performance REST/WebSocket APIs using FastAPI (Pydantic v2) - Implement and optimize agentic AI systems using frameworks like LangGraph, Deep Agents, AutoGen, and LangChain - Architect real-time, event-driven microservices using messaging queues like Apache Kafka - Design clean, testable, maintainable services using SOLID principles, Python async, and type hints - Integrate and optimize SQL, NoSQL, and vector databases (Postgres, MongoDB, ChromaDB, Pinecone) - Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls, backed by LLM observability and evaluation tooling (e.g., LangSmith, Langfuse) - Apply LLM safety guardrails (prompt-injection mitigation, PII handling, content moderation) in line with Banking, Insurance, and Healthcare compliance requirements - Stay current with emerging trends in GenAI, deep learning, and AI orchestration framework Skills and Competencies - Proven ability to architect and deliver end-to-end GenAI solutions and multi-agent systems - Strong software engineering discipline: testing (unit, integration, performance), code review, documentation - Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders - Strategic thinking and problem-solving with a focus on scalability and maintainability - Leadership capability: mentoring, technical guidance, and cross-functional collaboration