Key Responsibilities :

- Design and implement machine learning models from scratch when required

- Develop algorithms and perform model optimization and performance tuning

- Build scalable and production-ready ML pipelines for real-world applications

- Translate ambiguous business problems into well-defined mathematical formulations

- Collaborate with product and engineering teams to deploy models into production

- Continuously evaluate and improve model accuracy, efficiency, and robustness

- Perform data preprocessing, feature engineering, and rigorous model validation

- Quickly convert research papers into a poc code.

Required Skills & Qualifications :

- Strong proficiency in Python (mandatory)

- Deep understanding of machine learning algorithms (beyond library-level usage)

- Solid foundation in :

1. Linear Algebra

2. Probability & Statistics

3. Optimization Techniques

4. Multivariate Calculus and Stochastic Processes for understanding gradient-based optimization.

- Proven experience building and deploying ML models in production environments

- Hands-on experience with frameworks such as TensorFlow, PyTorch, or similar

- Data Manipulation : Advanced SQL and experience with data manipulation libraries (e.g., Polars or Pandas) for complex feature engineering.

- Time-Series & Forecasting : Proficiency in handling sequential data, anomaly detection, and predictive maintenance models.

- Strong problem-solving skills and ability to work with ambiguous, unstructured problems

- Ability to read, understand, and implement research papers