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

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

Responsibilities

Design and develop data models and analytical solutions, Optimize data analysis processes and methodologies, Ensure data solutions meet business and technical requirements, Provide technical support for data science projects, Collaborate with stakeholders to address data science needs.

Qualifications

Bachelor's/Master's in Engineering 2-5 years