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. As an official Anthropic partner, we work with cutting-edge foundation models to help organizations safely and effectively adopt advanced AI capabilities, serving clients across finance, healthcare, retail, and technology.
As an ML Solutions Architect, you will serve as the technical bridge between clients and our delivery teams. You will own the full pre-sales cycle — from leading discovery sessions and designing scalable ML and agentic AI architectures to crafting compelling proposals that win new business. This is a highly client-facing, senior-level role requiring deep technical expertise in ML and agentic systems, strong business acumen, and the ability to influence both technical and executive stakeholders across global accounts.
What You Will Do
Pre-Sales and Solution Design
- Lead technical discovery sessions with prospective clients to understand business problems and translate them into feasible ML solutions
- Design end-to-end ML architectures and author technical proposals, including scope, timeline, cost, and resource estimates
- Create and deliver compelling technical presentations and demonstrations to both technical and non-technical audiences
- Support General Managers in winning new business through technical leadership
Agentic Solutions Architecture
- Architect agentic AI solutions leveraging autonomous decision-making, tool orchestration, and LLM-based workflows
- Design MCP (Model Context Protocol) integration strategies for client environments
- Evaluate and recommend appropriate agent frameworks (LangGraph, Claude Agent SDK, and others) based on client use cases
- Develop reference architectures for common agentic patterns including RAG agents, multi-agent systems, and tool-using agents
- Build POC demonstrations showcasing agentic capabilities using AI-assisted development tools
- Advise clients on build-vs-buy decisions for agentic components and assess AgentOps requirements including monitoring, evaluation, and cost optimization
Client-Facing Technical Leadership
- Serve as the primary technical point of contact throughout the project lifecycle
- Manage technical stakeholder expectations and navigate complex organizational dynamics
- Build long-term trusted advisor relationships with clients
Internal Collaboration and Delivery Enablement
- Collaborate with delivery teams to ensure smooth project handoffs
- Provide technical guidance during project execution
- Contribute to reusable solution patterns, agentic accelerators, and Provectus AI toolkit documentation
- Mentor engineers on client communication and solution design
What You Will Bring
- 6–8+ years of demonstrated experience in ML or data science roles
- Proven track record in client-facing technical roles, including leading pre-sales or discovery engagements
- Portfolio of successfully architected and delivered ML solutions with a history of winning business through technical leadership
- Deep understanding of the full ML lifecycle from data ingestion through production deployment
- Experience designing scalable, production-grade ML architectures across multiple ML domains (RAG, Computer Vision, Time Series, Recommendation Systems, and others)
- Strong experience architecting LLM-based applications, including agentic systems
- Proficiency with agent design patterns, state management, and orchestration frameworks (LangGraph, LangChain agents, multi-agent systems)
- Hands-on experience with the Claude ecosystem: Claude Code, Claude Agent SDK, and Anthropic's tool ecosystem
- Working knowledge of Model Context Protocol (MCP) architecture for designing client integrations
- Demonstrated use of AI-assisted development tools (Cursor, GitHub Copilot, Claude Code) for rapid prototyping and POC development
- Advanced knowledge of AWS ML and data services including SageMaker, Bedrock, Lambda, and ECS
- Deep understanding of Amazon Bedrock agents, knowledge bases, and model hosting options
- Experience with serverless architectures (Lambda, API Gateway, Step Functions) for agentic workflows
- Knowledge of MLOps, LLMOps, and AgentOps practices including monitoring, evaluation, and cost optimization
- Ability to design cost-effective solutions with clear TCO analysis and trade-off assessment
- Understanding of data security, privacy, and compliance requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field, or equivalent experience with a demonstrable technical foundation
Nice to Have
- AWS Certifications (Solutions Architect Professional, ML Specialty)
- Experience in specific industries such as Finance, Healthcare, or Retail
- Knowledge of AI ethics and responsible AI practices
- Experience with edge ML and IoT deployments
- Published thought leadership (blogs, talks, or whitepapers)
- Contributions to open-source agent frameworks or MCP servers
- Understanding of ETL/ELT patterns, vector databases, data lakes, and real-time vs. batch processing architectures
- Previous consulting or professional services experience
- Multi-industry exposure
- Understanding of Azure and GCP services for comparative client discussions
What We Offer
