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ML Solutions Architect

hace 3 semanas

Perímetro Urbano Barranquilla, Colombia Provectus A tiempo completo

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 designRequirements:
  • 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