As an 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.
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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
– 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
– 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
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