Senior AI Architect

Senior AI Architect
Exp: 18+    
Visa: USC/
Remote with some travel required 
Look for candidates from GA, FL, TN, VA, SC, NC

About the Role
We are looking for a Senior AI Architect — a hands-on technologist who doesn't just design systems on whiteboards but builds them. This is a role for someone who can architect AI platforms end-to-end and then roll up their sleeves to write production code, wire up agents, and ship real solutions. You will define the AI architecture across cloud providers and AI frameworks, and personally drive critical builds from prototype to production.
If you thrive at the intersection of deep technical execution and architectural ownership, this role is for you.

Key Responsibilities
• Define and own AI solution architecture across cloud platforms (AWS, Google Cloud Platform, Azure), ensuring scalability, security, and cost-efficiency
• Hands-on development — personally design, build, and ship AI-powered applications, agents, and pipelines; this is not a slide-deck role
• Architect and implement agentic AI systems using frameworks such as LangChain and LangGraph, from design through deployment
• Lead full-stack engineering efforts encompassing Python and Node.js services, APIs, front-end interfaces, and data pipelines that power AI products
• Evaluate, integrate, and operationalize foundation models from providers including Anthropic (Claude), OpenAI, Hugging Face, and AWS Bedrock
• Establish MLOps and AgentOps practices — CI/CD for models, monitoring, drift detection, automated retraining, and agent lifecycle management
• Champion Responsible AI by embedding governance, fairness, and explainability tooling (e.g., Credo AI, Fiddler) into the development workflow
• Drive adoption of AI-native developer tools (e.g., GitHub Copilot, Devin, Windsurf) to accelerate engineering velocity across the team
• Collaborate with cross-functional teams, translating business requirements into robust AI architectures and guiding engineers through implementation
• Communicate technical direction clearly to leadership, clients, and cross-functional stakeholders

Required Skills & Experience
• 15+ years in software engineering with at least 3 years in architect or senior technical lead roles focused on AI/ML systems
• Proven hands-on builder — demonstrated ability to personally architect and develop production-grade AI applications, not just oversee them
• Deep proficiency in Python for AI/ML development (model integration, data processing, API development)
• Strong full-stack engineering skills — comfortable building end-to-end solutions including backend services, APIs, and front-end interfaces
• Production experience with all three major cloud AI platforms: AWS Bedrock, Azure AI, and Google Cloud Platform Vertex AI
• Hands-on experience building agentic AI systems using LangChain, LangGraph, or equivalent orchestration frameworks
• Deep working knowledge of foundation model providers — Anthropic, OpenAI, and Hugging Face — including prompt engineering, fine-tuning, RAG architectures, and open-source model hosting
• MLOps expertise — model versioning, experiment tracking, automated pipelines, monitoring, and production model lifecycle management
• AgentOps proficiency — monitoring, debugging, and managing autonomous AI agents in production throughout their full lifecycle
• Node.js / TypeScript experience for building AI-powered backend services or full-stack applications alongside Python
• Responsible AI tooling — hands-on experience with Credo AI (AI governance) and Fiddler (model monitoring and explainability)
• GitHub Copilot power-user experience with a track record of driving AI-assisted development adoption
• Hands-on use of AI-native development tools such as Devin, Windsurf, or similar AI coding assistants to accelerate delivery
• Excellent communication skills — ability to translate complex technical concepts for leadership and non-technical audiences
• Experience collaborating with and mentoring engineers in fast-paced, delivery-oriented environments

Qualifications
• Bachelor's or Master's degree in Computer Science, AI/ML, or a related technical field (or equivalent practical experience)
• A portfolio or track record of systems you personally built — we value what you've shipped over where you've worked
• Active engagement with the AI community (open-source contributions, publications, speaking, or technical blogging) is a plus