As an AI/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
GCP Expertise: Advanced knowledge of GCP 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
