Chief Machine Learning Architect

3 days ago

Allahabad, Uttar Pradesh, India beBeeMachine Full time ₹ 1,50,00,000 - ₹ 2,01,20,000
Machine Learning Lead

We are driving cutting-edge innovation by embracing a comprehensive approach to machine learning.

  • Spearhead applied research and development of advanced AI/ML solutions across domains including recommender systems, computer vision, speech processing, and generative AI agents.

As a lead data scientist, you will be responsible for:

  • Architecting and deploying end-to-end ML pipelines leveraging state-of-the-art deep learning, NLP, time-series modeling, and image analysis techniques.
    • Designing scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives.
    • Partnering with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact.
    • Providing ML thought leadership and leading high-performing teams, enforcing best practices from experimentation through production deployment.
    • Shaping AI strategy and guiding data-driven decision-making, influencing product innovation and operational excellence across the organization.

Key requirements include:

  • Graduate/Post-graduate degree from a top-tier institution 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 modeling, 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 (LangChain, Langraph, Pinecone, CrewAI) 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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