At REA India, we are driving cutting-edge innovation by embracing a comprehensive approach to machine learning. Unlike many organizations that specialize in just one area, here, 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. This diverse exposure to a range of ML technologies is a unique opportunity to broaden your skill set and make an impact at scale.

What will you drive?

- 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

Who are we looking for?

- 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