Location: Remote - LATAMSchedule: Full-time (8 hrs/day) — must have 4 hrs overlap with PSTAbout the RoleWe'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.