Enterprise Architect – AI, Cloud & Python

Position Title: Enterprise Architect – AI, Cloud & PythonLocation: Atlanta, GA (Onsite Only)Employment Type: W2 ContractDuration: 3-Year ProjectPosition OverviewWe are seeking an experienced Enterprise Architect to lead large-scale digital transformation initiatives with a focus on AI, Machine Learning, Cloud Engineering, and Python development. The ideal candidate will be a hands-on technical leader with deep expertise in modern AI architectures, LLM systems, and multi-cloud platforms (AWS and GCP).Key ResponsibilitiesArchitect and design AI and Large Language Model (LLM) systems using Python, LangChain, LangGraph, OpenAI, Bedrock, and Vertex AI.Develop and implement Retrieval-Augmented Generation (RAG) pipelines, agentic AI frameworks, and vector-based search solutions.Lead the multi-cloud architecture across AWS and GCP environments, leveraging microservices, Kubernetes, and serverless technologies.Design and optimize machine learning pipelines, data engineering frameworks, and scalable AI platforms for enterprise applications.Collaborate with executive leadership to define strategic AI objectives and technical roadmaps.Mentor cross-functional engineering teams and ensure adherence to best practices in architecture, DevOps, and deployment.Deliver high-quality, production-ready AI solutions that align with organizational goals.Required QualificationsMinimum of 15 years of experience in enterprise architecture or technology leadership.Strong hands-on Python coding skills; ability to design, build, and debug AI systems directly.Proven expertise in AI, LLMs, Cloud Architecture, Microservices, and DevOps.Deep technical understanding of AWS and GCP environments.Experience with Kubernetes, Docker, Terraform, and modern vector databases such as Pinecone, FAISS, and Redis.Excellent problem-solving skills, logical reasoning, and team leadership abilities.Technical StackLanguages & Frameworks: Python, LangChain, LangGraphAI Platforms: OpenAI, Bedrock, Vertex AICloud Platforms: AWS, GCPInfrastructure Tools: Kubernetes, Docker, TerraformDatabases: Pinecone, FAISS, Redis (Vector DBs)