Machine Learning Engineering Manager

Vintti; Teletrabalho; BRL 160.000 - 200.000; Tempo integral

Resumo da oferta

A leading technology firm is seeking a hands-on Machine Learning Engineering Manager who will lead cross-functional teams in designing, training, and deploying large-scale ML systems. The ideal candidate will drive the AI development lifecycle while mentoring engineers and collaborating with product and research teams to ensure impactful AI initiatives. This full-time, remote role requires strong leadership and deep ML expertise to manage projects from conception to deployment, ensuring responsible AI practices are met.

Qualificações

• Experience leading cross-functional teams in AI development.
• Hands-on knowledge of ML/LLM systems and project lifecycles.
• Expertise in data pipelines, distributed training, and model optimization.

Responsabilidades

• Lead and mentor ML engineers and data scientists.
• Manage end-to-end ML project lifecycle and MLOps best practices.
• Collaborate with teams to define objectives and KPIs.
• Communicate technical progress and results to stakeholders.

Conhecimentos

Deep ML expertise

Leadership

MLOps practices

Cloud budget management

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.