Chief Machine Learning Architect

1 day ago

Madurai, Tamil Nadu, India beBeeDataScientist Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Opportunity

We are driving innovation by embracing a comprehensive approach to machine learning. 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:
  • A graduate or postgraduate degree from a top-tier institute, combined with 5–8 years of experience in AI/ML, spanning NLP, deep learning, computer vision, and image processing, as well as 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.

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