Data Scientist

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

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)