Ml Solutions Architect
hace 2 semanas
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**:
- Classical ML: Foundation in traditional ML algorithms and when to use them
- 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
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