ML Solutions Architect

Core Responsibilities

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 (LangGraph, 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 AgentOps 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.

Technical Requirements

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 LangGraph, LangChain agents, and multi-agent orchestration patterns;
• AI-Assisted Workflows: Demonstrated experience with AI coding assistants (Cursor, GitHub Copilot, Claude Code) for rapid prototyping;
• Tool Ecosystem Design: Ability to architect function calling and tool use strategies for complex client requirements;
• AgentOps Understanding: Knowledge of agent monitoring, evaluation frameworks, and cost optimization strategies;
• POC Development: Ability to rapidly build compelling agentic demonstrations using AI-assisted development.
ML Breadth
• Multiple ML Domains: Experience across various ML applications (RAG, Computer Vision, Time Series, Recommendation, etc.);
• LLM Solutions: Strong experience in architecting LLM-based applications including agentic systems;
• Classical ML: Foundation in traditional ML algorithms and when to use them;
• Deep Learning: Understanding of neural network architectures and applications;
• MLOps/LLMOps/AgentOps: Knowledge of production ML infrastructure and DevOps practices for all ML paradigms.
Cloud and Infrastructure (AWS Required)
• AWS Expertise: Advanced knowledge of AWS ML and data services (SageMaker, Bedrock, Lambda, ECS, etc.);
• Amazon Bedrock: Deep understanding of Bedrock agents, knowledge bases, and model hosting options;
• Multi-Cloud Awareness: Understanding of Azure, GCP alternatives for comparative discussions;
• Serverless Architectures: Experience with Lambda, API Gateway, Step Functions for agentic workflows;
• Cost Optimization: Ability to design cost-effective solutions with clear TCO analysis;
• Security and Compliance: Understanding of data security, privacy, and compliance requirements.
Nice-to-Have Technical Skills
• AWS Certifications (Solutions Architect Professional, ML Specialty);
• Experience with specific industries (Finance, Healthcare, Retail, etc.);
• Knowledge of AI ethics and responsible AI practices;
• Experience with edge ML and IoT deployments;
• Published thought leadership (blogs, talks, whitepapers);
• Contributions to open-source agent frameworks or MCP servers.
• Data Pipelines: Understanding of ETL/ELT patterns and tools;
• Data Storage: Knowledge of databases, data lakes, vector databases, and warehouses;
• Data Quality: Understanding of data validation and monitoring;
• Real-time vs Batch: Ability to design for different data processing needs.

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;
Compensation for health insurance or sports coverage;
Access to the latest AI tools and subscriptions for professional development.