About this role

Design, train, and optimize ML models using PyTorch or TensorFlow for production-grade applications.
Build scalable data pipelines for feature engineering and model training using Pandas, Dask, or equivalent frameworks.
Implement model evaluation, hyperparameter tuning, and performance monitoring.
Develop and maintain ML workflows using Airflow, Kedro, and MLflow for reproducibility and traceability.
Automate model deployment and lifecycle management across environments (dev, staging, production).
Handle large-scale datasets efficiently using distributed computing frameworks (Dask, Spark).
Ensure data quality, consistency, and compliance with governance standards.
Implement model drift detection, performance tracking, and automated retraining strategies.
Document ML workflows, best practices, and operational guidelines.

5–7 years of experience in ML engineering or applied machine learning.
Strong proficiency in Python and libraries like Pandas, Dask, NumPy, Scikit-learn.
Hands-on experience with PyTorch or TensorFlow for model development.
Solid understanding of MLOps tools: Airflow, Kedro, MLflow (or equivalents).
Experience deploying ML models in production environments (APIs, batch jobs, streaming).
Strong problem-solving skills and ability to work in agile, fast-paced environments.

Senior Software Engineer, Machine Learning

Key skills