Lead AI Innovation Strategist

1 day ago

Erode, Tamil Nadu, India beBeeAI Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Artificial Intelligence Strategist

Key Responsibilities:

  • Develop and implement cutting-edge AI strategies that align with organizational goals.
  • Design and deploy large-scale ML, Deep Learning, and Generative AI systems.
  • Architect and 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.

Required Skills & Qualifications:

  • Master's/PhD in Computer Science, Data Science, AI/ML, or related field.
  • 10+ years of experience in Machine Learning and Deep Learning with at least 3+ years in leadership roles.
  • Proven track record in deploying AI solutions at scale across multiple domains (manufacturing, healthcare, finance, logistics, etc.).
  • 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).
  • Strong knowledge of model fine-tuning, transfer learning, RAG pipelines, and vector databases.
  • 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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