ML Solutions Architect

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