Data Scientist
This role focuses on building high-performance APIs and real-time microservices for agentic AI systems using FastAPI, LangChain, and Kafka, while integrating SQL, NoSQL, and vector databases. The candidate will design maintainable, async Python services following SOLID principles and optimize performance across the stack.
Skills required
About this role
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
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
Qualifications
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)