ML Solutions Architect at Provectus

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: 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 • GCP Expertise: Advanced knowledge of GCP 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