- PhD with 6+ years of experience, Master's degree with 8+ years of experience, or Bachelor's degree with 10+ years of experience in Computer Science, Engineering, Applied mathematics or related field.

- 7+ years of hands-on ML modeling/development experience.

- Experience in creating GenAI and Agentic AI applications.

- Solid working knowledge of the engineering components essential in a Gen AI application, including Vector DB, caching layer, chunking, and embedding.

- Knowledge of a variety of machine learning techniques (clustering, decision tree, bagging/boosting artificial neural networks, etc.) and their real-world advantages/drawbacks..

- Demonstrated track records in experimental design and executions.

- Strong skills in Python, PyTorch, Langchain, LangGraph etc. Solid background in algorithms and a range of ML models.

- Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level.

- Excellent analytical and problem-solving abilities with superb attention to detail.

- Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success.

Key Responsibilities :

- Design, develop, and deploy advanced machine learning models, including GenAI and Agentic AI applications.

- Lead the end-to-end development of AI/ML solutions, from experimental design and prototyping to production deployment.

- Architect and implement core engineering components essential for GenAI applications such as Vector Databases, caching layers, chunking mechanisms, and embedding strategies.

- Apply a wide range of machine learning techniques (clustering, decision trees, bagging/boosting, neural networks, etc.) and evaluate their applicability in real-world scenarios.

- Provide technical leadership and mentorship to junior data scientists, fostering a culture of innovation and continuous learning.

- Collaborate closely with Product, Engineering, and other cross-functional teams to align AI initiatives with business goals.

- Maintain a strong focus on code quality, performance optimization, and reproducibility of AI models.

- Communicate complex technical concepts effectively to both technical and non-technical stakeholders.