Data Scientist

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

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

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)