Chief Machine Learning Architect

2 days ago

Lucknow, Uttar Pradesh, India beBeeDataScientist Full time ₹ 20,00,000 - ₹ 40,00,000
AI Leadership Role

We are driving cutting-edge innovation by embracing a comprehensive approach to machine learning. As an AI leader, you will have the opportunity to work across all aspects of machine learning: from predictive models, time-series forecasting, and recommender systems, to computer vision and Gen AI-powered agents.

Key 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
Requirements
  • Graduate/Post-graduate from Tier-I institutes (IITs, IIITs, NITs) with 5–8 years of applied experience in AI/ML, spanning NLP, deep learning, computer vision, image processing, and with 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 (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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