ML Solutions Architect

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 business2. 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 relationships3. 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 designRequirements: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 problem2. 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 practices3. 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 compliance4. 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