Data Scientist

About the Role

**Job Description: 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 **Responsibilities: 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 **Qualifications: 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)