AI/ML Solutions Architect
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 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 relationship
- 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 template
Requirements: 1. 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 proble
- 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 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
- Data Architecture
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
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
Experience and Education: Demonstrated competency equivalent to 6-8+ years in ML/data science roles
Proven track record in client-facing technical roles
Experience leading pre-sales or discovery engagements
Portfolio of successfully architected and delivered ML solutions
History of winning business through technical leadership
Demonstrated experience with agentic AI architectures and AI-assisted development workflows Education: 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 expertise
Nice-to-Have experience: Previous consulting or professional services experience
Experience in multiple industries
Published content (blogs, videos, talks)
Track record of thought leadership in AI/ML
Open-source contributions to agent frameworks or MCP ecosystem
What We Offer: Competitive salary reflecting client-facing expertise
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