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 clientsUnderstand client business problems and translate them into ML solutionsDesign end-to-end ML architectures and technical proposalsCreate compelling technical presentations and demonstrationsEstimate project scope, timelines, cost, and resource requirementsSupport General Managers in winning new business 2. Client-Facing Technical Leadership (30%): Serve as the primary technical point of contact for clientsManage technical stakeholder expectationsPresent technical solutions to both technical and non-technical audiencesNavigate complex organizational dynamics and conflicting prioritiesEnsure client satisfaction throughout the project lifecycleBuild long-term trusted advisor relationships 3. Internal Collaboration and Handoff (20%): Collaborate with delivery teams to ensure smooth handoffProvide technical guidance during project executionContribute to the development of reusable solution patternsShare learnings and best practices with ML practiceMentor 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 problemsML Lifecycle: Deep understanding of the full ML lifecycle from data to deploymentSystem Design: Experience designing scalable, production-grade ML architecturesTrade-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 applicationsClassical ML: Foundation in traditional ML algorithms and when to use themDeep Learning: Understanding of neural network architectures and applicationsMLOps: Knowledge of production ML infrastructure and DevOps practices 3. Cloud and Infrastructure AWS Expertise: Advanced knowledge of AWS ML and data servicesMulti-Cloud Awareness: Understanding of Azure, GCP alternativesServerless Architectures: Experience with Lambda, API Gateway, etc.Cost Optimization: Ability to design cost-effective solutionsSecurity and Compliance: Understanding of data security, privacy, and compliance 4. Data Architecture Data Pipelines: Understanding of ETL/ELT patterns and toolsData Storage: Knowledge of databases, data lakes, and warehousesData Quality: Understanding of data validation and monitoringReal-time vs Batch: Ability to design for different data processing needs
