ML Solutions Architect

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
• 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