Principal Engineer - ML

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 Hands-on experience in Docker for Production deployment. 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).

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.