5 days ago Be among the first 25 applicants 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 Pre‑Sales and Solution Design (50%) 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 (30%) 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 Internal Collaboration and Handoff (20%) Collaborate with delivery teams to ensure smooth handoff Provide technical guidance during project execution Contribute to the development of reusable solution patterns Share learnings and best practices with ML practice Mentor engineers on client communication and solution design Requirements ML Architecture and Design Ability to architect 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) Quickly assess if ML is an appropriate solution for a problem Domain breadth across various ML applications (RAG, Computer Vision, Time Series, Recommendation, etc.) Strong experience in architecting LLM‑based applications Foundation in traditional ML algorithms and their appropriate use Understanding of neural network architectures and applications Knowledge of production ML infrastructure and DevOps practices (MLOps) Cloud and Infrastructure Advanced knowledge of AWS ML and data services Advanced knowledge of GCP ML and data services Understanding of Azure and other multi‑cloud alternatives Experience with serverless architectures (Lambda, API Gateway, etc.) Ability to design cost‑effective solutions Understanding of data security, privacy, and compliance Data Architecture Understanding of ETL/ELT patterns and tools Knowledge of databases, data lakes, and data warehouses Understanding of data validation and monitoring Ability to design for real‑time or batch processing needs We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Other Information Seniority level: Not Applicable Employment type: Full‑time Job function: Sales, Consulting, and Engineering Industries: Transportation, Logistics, Supply Chain and Storage Referrals increase your chances of interviewing at Provectus by 2x Get notified about new Solutions Architect jobs in Colombia, Huila, Colombia . #J-18808-Ljbffr