Metadata Library Jobs in Alpharetta, GA (NOW HIRING)

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 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, Postgres 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

•       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.