Chief Machine Learning Architect

2 days ago

Ajmer, Rajasthan, India beBeeInnovation Full time ₹ 40,00,000 - ₹ 50,00,000
Job Opportunity

We are at the forefront of innovation by embracing a comprehensive approach to machine learning. This diverse exposure to a range of ML technologies is a unique opportunity to broaden your skill set and make an impact at scale.

Main Responsibilities:
  • Spearhead applied research and development of advanced AI/ML solutions across domains including recommender systems, computer vision, speech processing, and generative AI agents
  • Architect and deploy end-to-end ML pipelines leveraging state-of-the-art deep learning, NLP, time-series modelling, and image analysis techniques
  • Design scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives
  • Partner with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact
  • Provide ML thought leadership and lead high-performing teams, enforcing best practices from experimentation through production deployment
  • Shape AI strategy and guide data-driven decision-making, influencing product innovation and operational excellence across the organization

The ideal candidate will have a graduate/post-graduate degree from a top-tier institute with 5–8 years of applied experience in AI/ML, spanning NLP, deep learning, computer vision, image processing, and introductory experience in building or fine-tuning generative AI models.

A proven leader (2+ years) in leading teams and driving ML projects, with a track record of building strong Data Science practices and fostering a high-performance culture.

The successful candidate will have expertise in ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn), Python, and hands-on experience in deep learning architectures (Transformers, CNNs, RNNs, LSTMs), statistical modelling, and scalable deployment using distributed systems.

They will also possess experience with LLM fine-tuning, RAG pipelines, embeddings, and prompt engineering; familiarity with GenAI ecosystems and integration into production applications.

A strong background in data wrangling, feature engineering, and MLOps, combined with the ability to collaborate across engineering, product, and business teams and communicate complex concepts effectively.

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