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