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Data Scientist Python AI​/ML

Job in Atlanta, Fulton County, Georgia, 30383, USA
Listing for: Accord Technologies Inc
Full Time position
Listed on 2026-02-24
Job specializations:
  • Software Development
    AI Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below
Position: Data Scientist with Python AI/ML

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:
    Lang Chain, Lang Graph, Semantic Kernel, Haystack, Llama Index, Auto Gen.
  • 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 (Postgre

    SQL, MySQL, etc.) for metadata and application storage.
  • Work with vector databases such as Redis, Postgre

    SQL 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:
    Lang Chain, Lang Graph, Semantic Kernel, Haystack, Llama Index, Auto Gen.
  • 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.
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