Overview
Title : Data Scientist with Python AI/ML
Location: Atlanta, GA (Inperson interview needed)
Position type: W2 contract.
Job Description
We are looking for a highly capable Technical Lead – Python & AI/ML with deep expertise in backend engineering, LLM-based applications, RAG architectures, and AI agent frameworks. You will lead the design, development, and deployment of production-grade AI systems built on Python, modern LLM tooling, retrieval engines, embeddings, and vector databases. This is a hands-on leadership role focused on building scalable and intelligent AI products. Investment Banking and financial domain experience is needed.
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.
Backend Engineering (Python)
- 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.
LLMs, RAG, and AI Agent Development
- 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.
Data & Vector Search Engineering
- 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, PostgreSQL with pgvector, Elasticsearch, Neo4j, or others.
- Implement and optimize search workflows using FAISS or similar similarity search libraries.
MLOps, Deployment & Observability
- Deploy AI services using Docker, container orchestration, and cloud environments.
- Implement monitoring for AI behavior, performance, error rates, and retrieval accuracy.
- Set up CI/CD pipelines for backend and AI components.
- Optimize inference cost, latency, and reliability.
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, and 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.