Chief Machine Learning Architect

1 day ago

Udaipur, Rajasthan, India beBeeArtificial Full time ₹ 2,00,00,000 - ₹ 4,00,00,000
Lead Data Scientist Position

Our organization seeks an accomplished Lead Data Scientist to lead its data science team. As a key member, you will be responsible for driving innovative solutions by embracing a comprehensive approach to machine learning.

You will have the opportunity to work across all aspects of machine learning, including predictive models, time-series forecasting, recommender systems, computer vision, and Gen 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:
  • 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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