Engineering Manager

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).