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.
Core Responsibilities
Technical Requirements
Success Metrics (First 90 Days)
Days 1-30:
- Shadow 3-5 pre-sales engagements;
- Build relationships with General Managers and sales team;
- Complete onboarding to Provectus solution catalog and AI toolkit;
- Contribute to at least 1 technical proposal;
- Demonstrate proficiency with Claude Code and AI-assisted development for POC creation.
Days 31-60:
- Lead 2-3 technical discovery sessions independently;
- Create compelling technical demonstrations including agentic AI capabilities;
- Successfully hand off 1-2 projects to delivery teams;
- Build rapport with key clients;
- Develop at least one reusable agentic solution pattern or reference architecture.
Days 61-90:
- Win at least 1 new client engagement through technical leadership;
- Establish yourself as trusted technical voice for agentic AI solutions;
- Contribute to at least 1 reusable solution asset or AI toolkit component;
- Receive positive feedback from clients and internal stakeholders;
- Successfully architect and propose at least one agentic solution to a client.
What We Offer
- High-visibility role working with diverse clients;
- Opportunity to shape solution offerings and practice direction;
- Work with cutting-edge ML, LLM, and agentic AI technologies;
- Global exposure across LATAM, Europe, and North America;
- Career path toward Practice Leadership or Principal Architect;
- Learning budget and conference attendance;
- Remote-first with regular client travel opportunities;
- Access to latest AI tools and subscriptions for professional development.
Application Process
- Initial Screening: Resume + preliminary discussion;
- Technical Assessment: System design + ML/agentic architecture discussion (2 hours);
- Case Study: Design solution for realistic client scenario including agentic components (presentation);
- Behavioral Interview: Client communication and stakeholder management;
- Final Round: Meet with Practice Leader and General Managers;
- Offer.