Advanced AI Architect

18 hours ago

Thrissur, Kerala, India beBeeAI Full time ₹ 2,50,00,000 - ₹ 3,50,00,000
Job Overview

A strategic and forward-thinking AI Architect will lead the design, architecture, and deployment of large-scale ML, Deep Learning, and Generative AI systems. This role requires a seasoned professional with experience in driving AI/ML strategy in alignment with organizational goals and Industry 4.0 initiatives.

The ideal candidate will have expertise in architecting and implementing state-of-the-art Generative AI solutions, including LLMs, VLMs, diffusion models, and multimodal systems. They will oversee the development of scalable ML pipelines, ensuring robustness, reliability, and efficiency. Additionally, they will provide technical leadership in MLOps, deployment strategies, and governance for AI models across edge and cloud.

This position requires collaboration with executive leadership, product managers, and domain experts to shape AI-driven innovation. The successful candidate will evaluate emerging research and technologies in AI/ML and recommend adoption strategies for enterprise-scale impact. Finally, they will mentor, coach, and grow data scientists, ML engineers, and research teams.

Requirements
  • 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.

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