Machine Learning Engineer Leader

13 hours ago

Meerut, Uttar Pradesh, India beBeeDataScientist Full time ₹ 1,51,67,661 - ₹ 2,51,83,601
Job Overview

We are seeking a seasoned professional to spearhead the development and deployment of cutting-edge AI systems. The ideal candidate will possess a strong background in Machine Learning, with at least 3+ years of experience in leadership roles.

The successful individual will have expertise in deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers, as well as hands-on experience with Generative AI models (LLMs, GANs, diffusion models, transformers, VLMs).

Key Responsibilities:

  • Design, architect, and deploy large-scale ML, Deep Learning, and Generative AI systems.
  • Drive AI/ML strategy in alignment with organizational goals and Industry 4.0 initiatives.
  • Implement state-of-the-art Generative AI solutions including LLMs, VLMs, diffusion models, and multimodal systems.
  • Oversee development of scalable ML pipelines, ensuring robustness, reliability, and efficiency.
  • Provide technical leadership in MLOps, deployment strategies, and governance for AI models across edge and cloud.
  • Collaborate with executive leadership, product managers, and domain experts to shape AI-driven innovation.
  • Evaluate emerging research and technologies in AI/ML and recommend adoption strategies for enterprise-scale impact.
  • Mentor, coach, and grow data scientists, ML engineers, and research teams.
Requirements

Education: Master's/PhD in Computer Science, Data Science, AI/ML, or related field.

Experience: 10+ years of experience in Machine Learning and Deep Learning with at least 3+ years in leadership roles.

Skills:

  • Strong expertise in deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
  • Hands-on experience with Generative AI models (LLMs, GANs, diffusion models, transformers, VLMs).
  • Deep understanding of distributed systems, model optimization, and high-performance computing for AI workloads.
  • Proven ability to architect AI solutions across cloud (AWS, Azure, GCP) and hybrid/edge deployments.
  • Experience with MLOps (MLflow, Kubeflow, Docker, Kubernetes, CI/CD) and responsible AI practices.
Preferred Skills

Experience in Industry 4.0 use cases such as digital twins, predictive maintenance, robotics, and computer vision in industrial environments.

Familiarity with Agentic AI frameworks, Model Context Protocol (MCP), and knowledge graph-based reasoning.

Understanding of reinforcement learning, edge AI, and federated learning.

Global experience working across geographies and diverse teams.

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