Engineering Manager (Python + Machine Learning)

Experience:

9+ years overall; 2+ years leading ML/LLM teams

Location:

Remote

Type:

Contractual

Role Overview

We are seeking a hands-on Machine Learning Engineering Manager to lead cross-functional teams building and deploying cutting-edge LLM and ML systems. In this role, you’ll drive the full lifecycle of AI development — from research and large-scale model training to production deployment — while mentoring top engineers and collaborating closely with research and infrastructure leaders.You’ll combine technical depth in deep learning and MLOps with leadership in execution and strategy, ensuring that all AI initiatives deliver reliable, high-performance systems that translate research breakthroughs into measurable business impact.This position is ideal for leaders who are still comfortable coding, optimizing large-scale training pipelines, and navigating the intersection of research, engineering, and product delivery.

Roles & Responsibilities

• Lead and mentor a cross-functional team of ML engineers, data scientists, and MLOps professionals.
• Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment.
• Collaborate with Research, Product, and Infrastructure teams to define goals, milestones, and success metrics.
• Provide technical direction on large-scale model training, fine-tuning, and distributed systems design.
• Implement best practices in MLOps, model governance, experiment tracking, and CI/CD for ML.
• Manage compute resources, budgets, and ensure compliance with data security and responsible AI standards.
• Communicate progress, risks, and results to stakeholders and executives effectively.
  

Required Skills & Qualifications

• 9+ yrs of strong background in Machine Learning, NLP, and modern deep learning architectures (Transformers, LLMs).
• Hands-on experience with frameworks such as PyTorch, TensorFlow, Hugging Face, or DeepSpeed
• 2+ yrs of proven experience managing teams delivering ML/LLM models in production environments.
• Knowledge of distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure).
• Familiarity with MLOps tools like MLflow, Kubeflow, or Vertex AI for scalable ML pipelines.
• Excellent leadership, communication, and cross-functional collaboration skills.
• Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD preferred).