ML Solutions Architect

As an **ML Solutions Architect**, you'll serve as the technical bridge between clients and delivery teams. Your role involves leading pre-sales technical discussions, designing ML architectures to address business problems, and ensuring that solutions are feasible, scalable, and aligned with client needs. This position requires a blend of 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 the ML practice - Mentor engineers on client communication and solution design ### Requirements: 1. **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 2. **ML Breadth** - Experience across various ML applications (RAG, Computer Vision, Time Series, Recommendation, etc.) - Strong experience in architecting LLM-based applications - Foundation in traditional ML algorithms and when to use them - Understanding of neural network architectures and applications - Knowledge of production ML infrastructure and DevOps practices 3. **Cloud and Infrastructure** - Advanced knowledge of AWS ML and data services - Understanding of Azure, GCP alternatives - Experience with serverless architectures - Ability to design cost-effective solutions - Understanding of data security, privacy, and compliance 4. **Data Architecture** - Understanding of ETL/ELT patterns and tools - Knowledge of databases and data lakes.