Requirements and responsibilities
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: 1. Pre-Sales and Solution Design (45%):Lead technical discovery sessions with prospective clientsUnderstand client business problems and translate them into ML solutionsDesign end-to-end ML architectures and technical proposalsCreate compelling technical presentations and demonstrationsEstimate project scope, timelines, cost, and resource requirementsSupport General Managers in winning new business2. Client-Facing Technical Leadership (25%):Serve as the primary technical point of contact for clientsManage technical stakeholder expectationsPresent technical solutions to both technical and non-technical audiencesNavigate complex organizational dynamics and conflicting prioritiesEnsure client satisfaction throughout the project lifecycleBuild long-term trusted advisor relationship3. Agentic Solutions Architecture (15%)Architect agentic AI solutions that leverage autonomous decision-making and tool orchestrationDesign MCP (Model Context Protocol) integration strategies for client environmentsEvaluate and recommend appropriate agent frameworks (LangGraph, Claude Agent SDK, etc.) for client use casesCreate POC demonstrations showcasing agentic capabilities using AI-assisted development toolsAdvise clients on build vs. buy decisions for agentic componentsDevelop reference architectures for common agentic patterns (RAG agents, multi-agent systems, tool-using agents)Assess AgentOps requirements including monitoring, evaluation, and cost optimization4. Internal Collaboration and Handoff (15%):Collaborate with delivery teams to ensure smooth handoffProvide technical guidance during project executionContribute to the development of reusable solution patterns and agentic acceleratorsShare learnings and best practices with ML practiceMentor engineers on client communication and solution designContribute to Provectus AI toolkit documentation and solution templateRequirements: 1. ML Architecture and DesignSolution Design: Ability to architect end-to-end ML systems for diverse business problemsML Lifecycle: Deep understanding of the full ML lifecycle from data to deploymentSystem Design: Experience designing scalable, production-grade ML architecturesTrade-off Analysis: Ability to evaluate technical approaches (cost, performance, complexity)Feasibility Assessment: Quickly assess if ML is an appropriate solution for a proble2. Agentic Engineering & AI-Assisted Development:Agentic Architecture: Deep understanding of agent design patterns, state management, and orchestration frameworksClaude Ecosystem: Hands-on experience with Claude Code, Claude Agent SDK, and Anthropic's tool ecosystemMCP Proficiency: Understanding of Model Context Protocol architecture for designing client integrationsAgent Frameworks: Practical knowledge of LangGraph, LangChain agents, and multi-agent orchestration patternsAI-Assisted Workflows: Demonstrated experience with AI coding assistants (Cursor, GitHub Copilot, Claude Code) for rapid prototypingTool Ecosystem Design: Ability to architect function calling and tool use strategies for complex client requirementsAgentOps Understanding: Knowledge of agent monitoring, evaluation frameworks, and cost optimization strategiesPOC Development: Ability to rapidly build compelling agentic demonstrations using AI-assisted development3. ML BreadthMultiple 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 systemsClassical ML: Foundation in traditional ML algorithms and when to use themDeep Learning: Understanding of neural network architectures and applicationsMLOps/LLMOps/AgentOps: Knowledge of production ML infrastructure and DevOps practices for all ML paradigms4. Cloud and InfrastructureAWS 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 optionsMulti-Cloud Awareness: Understanding of Azure, GCP alternatives for comparative discussionsServerless Architectures: Experience with Lambda, API Gateway, Step Functions for agentic workflowsCost Optimization: Ability to design cost-effective solutions with clear TCO analysisSecurity and Compliance: Understanding of data security, privacy, and compliance requirements5. Data ArchitectureData Pipelines: Understanding of ETL/ELT patterns and toolsData Storage: Knowledge of databases, data lakes, vector databases, and warehousesData Quality: Understanding of data validation and monitoringReal-time vs Batch: Ability to design for different data processing needsNice-to-Have Technical SkillsAWS Certifications (Solutions Architect Professional, ML Specialty)Experience with specific industries (Finance, Healthcare, Retail, etc.)Knowledge of AI ethics and responsible AI practicesExperience with edge ML and IoT deploymentsPublished thought leadership (blogs, talks, whitepapers)Contributions to open-source agent frameworks or MCP serversExperience and Education:Demonstrated competency equivalent to 6-8+ years in ML/data science rolesProven track record in client-facing technical rolesExperience leading pre-sales or discovery engagementsPortfolio of successfully architected and delivered ML solutionsHistory of winning business through technical leadershipDemonstrated experience with agentic AI architectures and AI-assisted development workflowsEducation: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related technical field or Equivalent experience with strong technical foundation and demonstrable expertiseNice-to-Have experience:Previous consulting or professional services experienceExperience in multiple industriesPublished content (blogs, videos, talks)Track record of thought leadership in AI/MLOpen-source contributions to agent frameworks or MCP ecosystemWhat We Offer:Competitive salary reflecting client-facing expertiseHigh-visibility role working with diverse clientsOpportunity to shape solution offerings and practice directionWork with cutting-edge ML, LLM, and agentic AI technologiesGlobal exposure across LATAM, Europe, and North AmericaCareer path toward Practice Leadership or Principal ArchitectLearning budget and conference attendanceRemote-first with regular client travel opportunitiesAccess to latest AI tools and subscriptions for professional development
Optionally, you can add more information later (benefits, pre-screening questions, etc.)