EXL—Assistant Manager

Job description

Description: Key Responsibilities 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 frameworks Minimum Qualifications • Bachelor's degree in Computer Science, Data Science, or related field • 3+ 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) 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 • Self-motivated with ability to work independently and drive projects to completion Requirements: Minimum Qualifications • Bachelor's degree in Computer Science, Data Science, or related field • 3+ 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 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 frameworks