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

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 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

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 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

3. 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

4. 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