AI/ML Solutions Architect

September 15, 2026 Auto Import

As an AI/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%):<ul> <li>Lead technical discovery sessions with prospective clients</li> <li>Understand client business problems and translate them into ML solutions</li> <li>Design end-to-end ML architectures and technical proposals</li> <li>Create compelling technical presentations and demonstrations</li> <li>Estimate project scope, timelines, cost, and resource requirements</li> <li>Support General Managers in winning new business</li> </ul> 2. Client-Facing Technical Leadership (30%):<ul> <li>Serve as the primary technical point of contact for clients</li> <li>Manage technical stakeholder expectations</li> <li>Present technical solutions to both technical and non-technical audiences</li> <li>Navigate complex organizational dynamics and conflicting priorities</li> <li>Ensure client satisfaction throughout the project lifecycle</li> <li>Build long-term trusted advisor relationships</li> </ul> 3. Internal Collaboration and Handoff (20%):<ul> <li>Collaborate with delivery teams to ensure smooth handoff</li> <li>Provide technical guidance during project execution</li> <li>Contribute to the development of reusable solution patterns</li> <li>Share learnings and best practices with ML practice</li> <li>Mentor engineers on client communication and solution design</li> </ul> Requirements: 1. ML Architecture and Design<ul> <li>Solution Design: Ability to architect end-to-end ML systems for diverse business problems</li> <li>ML Lifecycle: Deep understanding of the full ML lifecycle from data to deployment</li> <li>System Design: Experience designing scalable, production-grade ML architectures</li> <li>Trade-off Analysis: Ability to evaluate technical approaches (cost, performance, complexity)</li> <li>Feasibility Assessment: Quickly assess if ML is an appropriate solution for a problem</li> </ul> 2. ML Breadth<ul> <li>Multiple ML Domains: Experience across various ML applications (RAG, Computer Vision, Time Series, Recommendation, etc.)</li> <li>LLM Solutions: Strong experience in architecting LLM-based applications</li> <li>Classical ML: Foundation in traditional ML algorithms and when to use them</li> <li>Deep Learning: Understanding of neural network architectures and applications</li> <li>MLOps: Knowledge of production ML infrastructure and DevOps practices</li> </ul> 3. Cloud and Infrastructure<ul> <li>AWS Expertise: Advanced knowledge of AWS ML and data services</li> <li>GCP Expertise: Advanced knowledge of GCP ML and data services</li> <li>Multi-Cloud Awareness: Understanding of Azure, GCP alternatives</li> <li>Serverless Architectures: Experience with Lambda, API Gateway, etc.</li> <li>Cost Optimization: Ability to design cost-effective solutions</li> <li>Security and Compliance: Understanding of data security, privacy, and compliance</li> </ul> 4. Data Architecture<ul> <li>Data Pipelines: Understanding of ETL/ELT patterns and tools</li> <li>Data Storage: Knowledge of databases, data lakes, and warehouses</li> <li>Data Quality: Understanding of data validation and monitoring</li> <li>Real-time vs Batch: Ability to design for different data processing needs</li> </ul>

Back to blog