ML Solutions Architect (GenAI)

Provectus; Cali; A distancia; COP 120.000.000 - 180.000.000; Jornada completa

Descripción de la vacante

A technology solutions provider based in Colombia seeks an experienced ML Solutions Architect to act as the technical bridge between clients and teams. The role involves leading pre-sales discussions, designing scalable ML architectures, and ensuring client satisfaction throughout the project lifecycle. Candidates should have strong expertise in ML systems, cloud services, and a solid understanding of various ML applications and data processes.

Formación

• Ability to architect end-to-end ML systems for diverse business problems.
• Deep understanding of the full ML lifecycle from data to deployment.
• Experience designing scalable, production-grade ML architectures.
• Ability to evaluate technical approaches (cost, performance, complexity).
• Quickly assess if ML is an appropriate solution for a problem.
• Experience across various ML applications (RAG, CV, Time Series, Recommender, etc.).
• Strong experience in architecting LLM-based applications.
• Foundation in traditional ML algorithms and when to use them.
• Understanding of neural network architectures and applications.
• Knowledge of production ML infrastructure and DevOps practices.
• Advanced knowledge of AWS ML and data services.
• Advanced knowledge of GCP ML and data services.
• Understanding of Azure, GCP alternatives.
• Experience with Lambda, API Gateway, etc.
• Ability to design cost-effective solutions.
• Understanding of data security, privacy, and compliance.
• Understanding of ETL/ELT patterns and tools.
• Knowledge of databases, data lakes, and warehouses.
• Understanding of data validation and monitoring.
• Ability to design for Real-time vs Batch data processing.

Responsabilidades

• Lead technical discovery sessions with prospective clients.
• Manage technical stakeholder expectations.
• Collaborate with delivery teams to ensure smooth handoff.
• Estimate project scope, timelines, cost, and resource requirements.
• Serve as the primary technical point of contact for clients.
• Manage technical stakeholder expectations and present to diverse audiences.
• Collaborate with delivery teams to ensure smooth handoff.
• Provide technical guidance during project execution.
• Contribute to reusable solution patterns and share learnings.
• Mentor engineers on client communication and solution design.

Conocimientos

Architect end-to-end ML systems

ML lifecycle understanding

Scalable ML architectures

Technical approach evaluation

ML applications experience

LLM-based applications

Traditional ML algorithms

Neural network architectures

Production ML infrastructure knowledge

AWS ML and data services

GCP ML and data services

Azure awareness

Cost-effective solutions design

Data security and compliance

Cost Optimization

Security and Compliance

Data Pipelines

Data Storage

Data Quality

Real-time vs Batch

Herramientas

Lambda

API Gateway

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

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

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

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

• 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
• ML Breadth: 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
• 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
• 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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.