Machine Learning Engineering Manager

Há 7 dias

Porto Alegre, Brasil Vintti Tempo inteiro

🌎 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.

🎯 Key Responsibilities

- 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.

🧩 Required Skills & Qualifications

- 5+ years of experience in Machine Learning, NLP, and Deep Learning (Transformers, LLMs).
- 2+ years leading teams delivering ML/LLM systems in production.
- Strong proficiency in Python and frameworks like PyTorch, TensorFlow, Hugging Face, DeepSpeed.
- Experience with distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure).
- Knowledge of MLOps tools (MLflow, Kubeflow, Vertex AI, etc.).
- Excellent leadership, communication, and cross-functional collaboration skills.
- Bachelor's/Master’s in Computer Science, Engineering, or related field (PhD preferred).

💡 Nice to Have

- Experience training or fine-tuning foundation models.
- Contributions to open-source ML/LLM frameworks.
- Knowledge of Responsible AI practices, bias mitigation, and model interpretability.

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