Data Scientist with Python AI/ML

Key Responsibilities

• Lead the architecture and development of LLM-driven applications, AI agents, and RAG-based systems.
• Provide technical guidance, conduct code reviews, and mentor junior team members.
• Drive best practices in Python backend engineering, API development, and AI system design.
• Build and maintain backend services using FastAPI or Flask.
• Develop scalable API endpoints for AI applications, embeddings, and retrieval systems.
• Ensure backend code quality, modularity, performance, and maintainability.
• Build AI applications using: LangChain, LangGraph, Semantic Kernel, Haystack, LlamaIndex, AutoGen.
• Develop autonomous or semi-autonomous AI agents with tool calling and workflow graphs.
• Implement Retrieval-Augmented Generation (RAG), embedding pipelines, chunking strategies, reranking, and grounding techniques.
• Work with OpenAI SDK and other LLM providers (Anthropic, Azure OpenAI, Cohere, etc.).
• Manage prompt engineering, prompt routing, safety guardrails, and evaluation metrics.
• Build data pipelines for indexing, embeddings, and retrieval workflows.
• Work with SQL databases (PostgreSQL, MySQL, etc.) for metadata and application storage.
• Work with vector databases such as: Redis, Postgres with pgvector, Elasticsearch, Neo4j, or others.
• Implement and optimize search workflows using FAISS or similar similarity search libraries.
• Cross-functional Collaboration:
• Collaborate with product, data engineering, and business teams to understand requirements.
• Translate business problems into scalable AI architectures and deliver practical solutions.
• Communicate technical decisions, trade-offs, and progress to stakeholders.

Required Qualifications

• Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or related fields.
• 10+ years of experience in Python backend development.
• Strong proficiency in FastAPI or Flask.
• Strong working knowledge of SQL databases (Postgres, MySQL, etc.).
• Hands-on expertise with vector databases: Redis, Postgres/pgvector, Elasticsearch, or Neo4j.
• Practical experience with FAISS for similarity search.
• Hands-on experience with modern LLM frameworks: LangChain, LangGraph, Semantic Kernel, Haystack, LlamaIndex, AutoGen.
• Strong understanding of: Embeddings & vector search, RAG pipelines, Retrieval optimization, Chunking strategies, Document loaders & indexing.
• Experience building AI apps using OpenAI SDK or similar.
• Experience deploying APIs/services using Docker and cloud environments.
• Leadership experience: guiding teams, conducting reviews, driving architecture decisions.