GenAI Scientist (MCP + Computer Vision)

Job Title: GenAI Scientist

Location: Atlanta, GA 30308 (REMOTE/Hybrid)

Contract duration: 6+ Months, Long-term.

Levels: Junior, Intermediate, Senior

Industry: Rail Transportation.

What You’ll Work On

• Document Intelligence: Extracting and analyzing large volumes of corporate documents using NLP and AI


• Conversational AI: Building enterprise chat systems for knowledge discovery and decision support


• GenAI & LLMs: Developing, optimizing, and monitoring LLM and RAG pipelines


• Enterprise Integration: Integrating AI solutions with corporate systems (OAuth, LDAP, cloud services)


• Scalable Platforms: Designing microservices-based, production-ready AI solutions

Key Responsibilities

• Enhance and maintain an enterprise AI chat and document intelligence platform


• Develop and optimize RAG pipelines and LLM integrations


• Build backend APIs and AI tools for scalable, multi-team use


• Collaborate with business teams to translate document and data needs into AI solutions


• Support production deployments with monitoring, logging, and performance optimization

Core Technologies

Languages: Python, JavaScript/TypeScript

AI/ML: LLMs (Claude, GPT), LangChain, vector search, NLP libraries

Backend: FastAPI, Node.js, Express

Cloud & DevOps: AWS (ECS, EKS, S3, Lambda, Bedrock), Docker, Kubernetes, CI/CD

Databases: MongoDB, vector databases

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, ML, Linguistics, or related field


• 2+ years of experience in NLP, AI, or LLM-based development


• Strong experience building APIs and production AI systems


• Familiarity with RAG architectures, embeddings, and semantic search


• Experience deploying AI solutions in cloud environments


• Experience in MCP (Model Context Protocol)


• Computer Vision expertise required, including image classification, object detection, and semantic segmentation; hands-on use of OpenCV and scikit-image (skimage); and strong knowledge of CNN architectures for classification, detection, and segmentation.

Preferred Qualifications

• Master’s or PhD in a related field


• Experience with MLOps, microservices, and enterprise authentication systems


• Knowledge of advanced NLP techniques and observability tooling

GenAI Scientist (Junior–Senior) — This role focuses on building and operating enterprise-grade generative AI solutions for the rail transportation industry, including document intelligence systems for large-scale corporate document extraction and analysis, conversational AI for knowledge discovery and decision support, and the development, optimization, and monitoring of LLM and RAG pipelines. Responsibilities include enhancing and maintaining an enterprise AI chat and document intelligence platform, developing RAG pipelines and LLM integrations, building scalable backend APIs and AI tools, collaborating with business teams to translate document and data requirements into AI solutions, and supporting production deployments with monitoring, logging, and performance optimization. Required qualifications include a bachelor’s degree in Computer Science, Data Science, Machine Learning, Linguistics, or a related field; 2+ years of experience in NLP, AI, or LLM-based development; strong experience building APIs and production AI systems; familiarity with RAG architectures, embeddings, semantic search, vector databases, and MCP (Model Context Protocol); and experience deploying AI solutions in cloud environments. Core technical requirements include Python and JavaScript/TypeScript; LLMs (e.g., Claude, GPT), LangChain, NLP libraries, and vector search; backend frameworks such as FastAPI, Node.js, and Express; AWS services (ECS, EKS, S3, Lambda, Bedrock); Docker, Kubernetes, CI/CD; and MongoDB. Preferred qualifications include a master’s or PhD, experience with MLOps, microservices, enterprise authentication systems (e.g., OAuth, LDAP), advanced NLP techniques, and observability tooling.

Computer Vision expertise required, including image classification, object detection, and semantic segmentation; hands-on use of OpenCV and scikit-image (skimage); and strong knowledge of CNN architectures for classification, detection, and segmentation.