Data Architectural Leader

2 days ago

Kottayam, Kerala, India beBeeDataScience Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Lead Data Scientist

Job Summary:

We are seeking a seasoned Lead Data Scientist to lead the design, architecture, and deployment of large-scale Machine Learning, Deep Learning, and Generative AI systems.

Key Responsibilities:

  • Design, architect, and deploy large-scale Machine Learning, Deep Learning, and Generative AI systems
  • Drive AI/ML strategy in alignment with organizational goals and Industry 4.0 initiatives
  • 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:

  • 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
  • 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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