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ML Solutions Architect at Provectus. Location Information: . 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.. \n. Core Responsibilities:. 1. 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. 2. 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. 3. 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:. 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 problem. 2. 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. - Classical ML: Foundation in traditional ML algorithms and when to use them. - Deep Learning: Understanding of neural network architectures and applications. - MLOps: Knowledge of production ML infrastructure and DevOps practices. 3. Cloud and Infrastructure. - AWS Expertise: Advanced knowledge of AWS ML and data services. - Multi-Cloud Awareness: Understanding of Azure, GCP alternatives. - Serverless Architectures: Experience with Lambda, API Gateway, etc.. - Cost Optimization: Ability to design cost-effective solutions. - Security and Compliance: Understanding of data security, privacy, and compliance. 4. Data Architecture. - Data Pipelines: Understanding of ETL/ELT patterns and tools. - Data Storage: Knowledge of databases, data lakes, and warehouses. - Data Quality: Understanding of data validation and monitoring. - Real-time vs Batch: Ability to design for different data processing needs. \n. Please mention the word **TRUTHFULLY** and tag RMTA0LjE1NC4yMDcuNTI= when applying to show you read the job post completely (#RMTA0LjE1NC4yMDcuNTI=). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human..