ML Solutions Architect

Provectus; Medellín; Presencial; COP 271.897.000 - 388.425.000; Jornada completa

Descripción de la vacante

A leading technology solutions company in Medellín is seeking an experienced ML Solutions Architect to design and implement end-to-end machine learning architectures. This client-facing role requires strong technical expertise and excellent communication skills. Ideal candidates will have extensive knowledge of AWS services and hands-on experience in various ML domains including deep learning and MLOps.

Formación

• Deep understanding of the full ML lifecycle from data to deployment.
• Ability to architect end-to-end ML systems for diverse business problems.
• Advanced knowledge of AWS ML and data services.

Responsabilidades

• Lead technical discovery sessions with prospective clients.
• Design end-to-end ML architectures and technical proposals.
• Serve as the primary technical point of contact for clients.

Conocimientos

ML Architecture and Design

ML Breadth

Cloud and Infrastructure

Data Architecture

Herramientas

AWS

ETL/ELT tools

Descripción del empleo

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