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1 week ago

nashik, India beBeeEngineering Full time

Role Description:We seek a skilled Machine Learning Engineering Manager to lead cross-functional teams building and deploying cutting-edge large language models (LLMs) and machine learning (ML) systems.This role combines technical expertise in deep learning and model operations with leadership in execution and strategy, ensuring that AI initiatives deliver reliable, high-performance systems that drive measurable business impact.Lead and mentor a 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 resources, budgets, and ensure compliance with data security and responsible AI standards.Communicate progress, risks, and results to stakeholders and executives effectively.Requirements: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 DeepSpeed2+ yrs of proven experience managing teams delivering ML/LLM models in production environments.Knowledge of distributed training, GPU/TPU optimisation, 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,Nice to Have:Experience training or fine-tuning foundation models.Contributions to open-source ML or LLM frameworks.Understanding of Responsible AI, bias mitigation, and model interpretability.Benefits:Work from anywhere.Opportunity to work on cutting-edge AI projects.Evaluation Process:Two rounds of interviews.

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