Senior AI Solutions Architect, AWS

Lead technical discovery sessions with prospective clients to understand business problems and translate them into feasible ML solutions Design end-to-end ML architectures and author technical proposals, including scope, timeline, cost, and resource estimates Create and deliver compelling technical presentations and demonstrations to both technical and non-technical audiences Support General Managers in winning new business through technical leadership Architect agentic AI solutions leveraging autonomous decision‑making, tool orchestration, and LLM‑based workflows Design MCP (Model Context Protocol) integration strategies for client environments Evaluate and recommend appropriate agent frameworks (LangGraph, Claude Agent SDK, and others) based on client use cases Develop reference architectures for common agentic patterns including RAG agents, multi‑agent systems, and tool‑using agents Build POC demonstrations showcasing agentic capabilities using AI‑assisted development tools Advise clients on build‑vs‑buy decisions for agentic components and assess AgentOps requirements including monitoring, evaluation, and cost optimization Serve as the primary technical point of contact throughout the project lifecycle Manage technical stakeholder expectations and navigate complex organizational dynamics Build long‑term trusted advisor relationships with clients Collaborate with delivery teams to ensure smooth project handoffs Provide technical guidance during project execution Contribute to reusable solution patterns, agentic accelerators, and Provectus AI toolkit documentation Mentor engineers on client communication and solution design Requirements 6–8+ years of demonstrated experience in ML or data science roles Proven track record in client‑facing technical roles, including leading pre‑sales or discovery engagements Portfolio of successfully architected and delivered ML solutions with a history of winning business through technical leadership Deep understanding of the full ML lifecycle from data ingestion through production deployment Experience designing scalable, production‑grade ML architectures across multiple ML domains (RAG, Computer Vision, Time Series, Recommendation Systems, and others) Strong experience architecting LLM‑based applications, including agentic systems Proficiency with agent design patterns, state management, and orchestration frameworks (LangGraph, LangChain agents, multi‑agent systems) Hands‑on experience with the Claude ecosystem: Claude Code, Claude Agent SDK, and Anthropic's tool ecosystem Working knowledge of Model Context Protocol (MCP) architecture for designing client integrations Demonstrated use of AI‑assisted development tools (Cursor, GitHub Copilot, Claude Code) for rapid prototyping and POC development Advanced knowledge of AWS ML and data services including SageMaker, Bedrock, Lambda, and ECS Deep understanding of Amazon Bedrock agents, knowledge bases, and model hosting options Experience with serverless architectures (Lambda, API Gateway, Step Functions) for agentic workflows Knowledge of MLOps, LLMOps, and AgentOps practices including monitoring, evaluation, and cost optimization Ability to design cost‑effective solutions with clear TCO analysis and trade‑off assessment Understanding of data security, privacy, and compliance requirements Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field, or equivalent experience with a demonstrable technical foundation Certifications & Qualifications Bachelor's degree in Computer Science Master's degree in Data Science Master's degree in Engineering #J-18808-Ljbffr