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.

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

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