Machine Learning Engineering Manager

Vintti; Teletrabalho; BRL 649.000 - 812.000; Tempo integral

Resumo da oferta

A tech-driven company is seeking a Machine Learning Engineering Manager to lead and mentor teams in the development of large-scale AI systems. This role involves managing the complete project lifecycle from data preparation to deployment, ensuring best practices in MLOps are followed. The ideal candidate will have a proven track record in ML engineering, excellent leadership skills, and the ability to communicate with diverse stakeholders. A remote working arrangement is available, focusing on collaboration with cross-functional teams.

Qualificações

• Proven experience in leading Machine Learning engineering teams.
• Strong expertise in designing and deploying large-scale ML systems.
• Ability to communicate technical information effectively to various stakeholders.

Responsabilidades

• Lead and mentor ML engineers and data scientists.
• Manage the entire ML project lifecycle including data pipelines and deployment.
• Implement best practices for MLOps and ensure data security standards.

Conhecimentos

Machine Learning

MLOps

Leadership

Collaboration

Ferramentas

Cloud Computing

Descrição da oferta de emprego

Location : Remote - LATAM Schedule : Full-time (8 hrs / day) — must have 4 hrs overlap with PST?

About the Role: We're looking for a hands‑on Machine Learning Engineering Manager to lead cross‑functional teams in designing, training, and deploying large-scale ML and LLM systems. You'll drive the full lifecycle of AI development — from research and experimentation to distributed training and production deployment — while mentoring top‑tier engineers and partnering closely with product, research, and infra leaders. This role blends deep ML / MLOps expertise with strong leadership and execution, ensuring all AI initiatives translate into measurable business impact.

• Lead and mentor ML engineers, data scientists, and MLOps professionals.
• Manage end‑to‑end ML / LLM project lifecycle: data pipelines, training, evaluation, deployment, and monitoring.
• Provide technical direction for distributed training, large-scale model optimization, and system architecture.
• Collaborate with Research, Product, and Infrastructure teams to define objectives, milestones, and KPIs.
• Implement MLOps best practices: experiment tracking, CI / CD, model governance, observability.
• Manage compute resources, cloud budgets, and enforce Responsible AI + data security standards.
• Communicate technical progress, blockers, and results clearly to leadership and stakeholders.