AI Solutions Architect AWS - Provectus

About the Role

Provectus is a global AI and cloud consulting company helping enterprises turn artificial intelligence and data into production-ready business solutions. We specialize in designing, building, and scaling end-to-end AI/ML systems, data platforms, and cloud-native architectures, with strong expertise in AWS, MLOps, and enterprise-grade AI delivery . As an official Anthropic partner , we work with cutting-edge foundation models to help organizations safely and effectively adopt advanced AI capabilities, serving clients across finance, healthcare, retail, and technology.

As an ML Solutions Architect , you will serve as the technical bridge between clients and our delivery teams. You will own the full pre-sales cycle — from leading discovery sessions and designing scalable ML and agentic AI architectures to crafting compelling proposals that win new business. This is a highly client-facing, senior-level role requiring deep technical expertise in ML and agentic systems, strong business acumen, and the ability to influence both technical and executive stakeholders across global accounts.

What You Will Do

Pre-Sales and Solution Design

• 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

Agentic Solutions Architecture

• 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

Client-Facing Technical Leadership

• 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

Internal Collaboration and Delivery Enablement

• Collaborate with delivery teams to ensure smooth project handoffs<