Chief Machine Learning Engineer

20 hours ago

Kanpur, Uttar Pradesh, India beBeeDataScientist Full time ₹ 17,53,566 - ₹ 23,14,830
About Our Team

We drive innovation by embracing a holistic approach to machine learning.

We specialize in all aspects of machine learning: from predictive models, time-series forecasting, and recommender systems, to computer vision and AI-powered agents.

  • This diverse exposure to a range of ML technologies is a unique opportunity to broaden your skill set and make an impact at scale.
Key Responsibilities

You will 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.

You will 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.

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.
Requirements

Postgraduate degree from top-tier institutions 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.

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

Advanced ML Expertise: Proficiency in ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn) and Python, with hands-on experience in deep learning architectures (Transformers, CNNs, RNNs, LSTMs), statistical modelling, and scalable deployment using distributed systems (e.g., Spark, Hadoop).

  • Generative AI & Applied Innovation: Practical experience with LLM fine-tuning, RAG pipelines, embeddings, and prompt engineering; familiarity with GenAI ecosystems and integration into production applications.

End-to-End Impact & Collaboration: Strong background in data wrangling, feature engineering, and MLOps, combined with proven ability to collaborate across engineering, product, and business teams and communicate complex concepts effectively.

  • A passion for solving complex problems with the courage to take research risks and drive innovation.

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