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. Core Responsibilities Lead technical discovery sessions with prospective clients (50% of effort) Translate client business problems into ML solutions Design end‑to‑end ML architectures and technical proposals Create compelling technical presentations and demonstrations Estimate project scope, timelines, cost, and resources Support General Managers in winning new business Serve as the primary technical point of contact for clients Manage technical stakeholder expectations and navigate complex organizational dynamics Present solutions to technical and non‑technical audiences Ensure client satisfaction throughout the project lifecycle Build long‑term trusted advisor relationships Collaborate with delivery teams to ensure smooth handoff Provide technical guidance during project execution Contribute to reusable solution patterns and share best practices Mentor engineers on client communication and solution design Requirements Design end‑to‑end ML systems for diverse business problems Deep understanding of the full ML lifecycle from data to deployment Experience designing scalable, production‑grade ML architectures Ability to evaluate technical approaches (cost, performance, complexity) Ability to quickly assess if ML is appropriate for a problem Experience across multiple ML domains (RAG, Computer Vision, Time Series, Recommendation, etc.) Strong experience in architecting LLM‑based applications Fundamentals of classical ML algorithms and when to use them Understanding of deep learning architectures and applications Knowledge of production ML infrastructure and MLOps practices Advanced knowledge of AWS ML and data services; awareness of Azure and GCP alternatives Experience with serverless architectures (Lambda, API Gateway, etc.) Ability to design cost‑effective solutions and ensure data security and compliance Understanding of ETL/ELT pipelines, data lakes, warehouses, and data quality monitoring Ability to design for real‑time versus batch processing needs Referrals increase your chances of interviewing at Provectus by 2x. Employment type: Full‑time | Remote Work | Location: Flexible #J-18808-Ljbffr