ML Solutions Architect

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.

We are an official Anthropic partner
, working with cutting‑edge foundation models to help organizations safely and effectively adopt advanced AI capabilities.

Our consulting teams operate across industries such as finance, healthcare, retail, and technology, delivering solutions with measurable business impact through hands‑on engineering and advisory.

As an ML Solutions Architect
, you’ll be the technical bridge between clients and delivery teams. You’ll lead pre‑sales technical discussions, design ML architectures that solve business problems, and ensure solutions are feasible, scalable, and aligned with client needs.

This is a highly client‑facing role requiring both deep technical expertise and strong communication skills. In the era of Generative AI and autonomous systems, you’ll also be responsible for architecting agentic solutions that leverage LLMs, tool ecosystems, and AI‑assisted workflows to deliver transformative value to clients.

Pre‑Sales and Solution Design (45%)

• Lead technical discovery sessions with prospective clients;
• Understand client business problems and translate them into ML solutions;
• Design end‑to‑end ML architectures and technical proposals;
• Create compelling technical presentations and demonstrations;
• Estimate project scope, timelines, cost, and resource requirements;
• Support General Managers in winning new business.

Client‑Facing Technical Leadership (25%)

• Serve as the primary technical point of contact for clients;
• Manage technical stakeholder expectations;
• Present technical solutions to both technical and non‑technical audiences;
• Navigate complex organizational dynamics and conflicting priorities;
• Ensure client satisfaction throughout the project lifecycle;
• Build long‑term trusted advisor relationships.

Agentic Solutions Architecture (15%)

• Architect agentic AI solutions that leverage autonomous decision‑making and tool orchestration;
• Design MCP (Model Context Protocol) integration strategies for client environments;
• Evaluate and recommend appropriate agent frameworks (Lang Graph, Claude Agent SDK, etc.) for client use cases;
• Create POC demonstrations showcasing agentic capabilities using AI‑assisted development tools;
• Advise clients on build vs. buy decisions for agentic components;
• Develop reference architectures for common agentic patterns (RAG agents, multi‑agent systems, tool‑using agents);
• Assess Agent Ops requirements including monitoring, evaluation, and cost optimization.

Internal Collaboration and Handoff (15%)

• Collaborate with delivery teams to ensure smooth handoff;
• Provide technical guidance during project execution;
• Contribute to the development of reusable solution patterns and agentic accelerators;
• Share learnings and best practices with ML practice;
• Mentor engineers on client communication and solution design;
• Contribute to Provectus AI toolkit documentation and solution templates.

ML Architecture and Design

• Solution Design:
  Ability to architect end‑to‑end ML systems for diverse business problems;
• ML Lifecycle:
  Deep understanding of the full ML lifecycle from data to deployment;
• System Design:
  Experience designing scalable, production‑grade ML architectures;
• Trade‑off Analysis:
  Ability to evaluate technical approaches (cost, performance, complexity);
• Feasibility Assessment:
  Quickly assess if ML is an appropriate solution for a problem.

Agentic Engineering & AI‑Assisted Development

• Agentic Architecture:
  Deep understanding of agent design patterns, state management, and orchestration frameworks;
• Claude Ecosystem:
  Hands‑on experience with Claude Code, Claude Agent SDK, and Anthropic's tool ecosystem;
• MCP Proficiency:
  Understanding of Model Context Protocol architecture for designing client integrations;
• Agent Frameworks:
  Practical knowledge of Lang Graph, Lang Chain agents, and multi‑agent…