AI/ML Solutions Architect

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%):

• 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

1. 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

2. 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

1. 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

2. 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

3. 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