AI Architect

2 days ago

Vellore, Tamil Nadu, India beBeeArtificial Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Description:

We are seeking an experienced AI Architect to lead the development of large-scale machine learning and deep learning systems.

The ideal candidate will have a proven track record in deploying AI solutions at scale across multiple domains and possess strong expertise in deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.

A successful candidate will also have hands-on experience with Generative AI models (LLMs, GANs, diffusion models, transformers, VLMs) and a deep understanding of distributed systems, model optimization, and high-performance computing for AI workloads.

Key Responsibilities:
  • Lead the design, architecture, and deployment of large-scale ML, Deep Learning, and Generative AI systems.
  • Drive AI/ML strategy in alignment with organizational goals and industry 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.
  • 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 or 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.

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