About the role
Responsibilities:
- Design, build, and deploy machine learning models for predictive analytics, classification, recommendation systems, NLP, or computer vision use cases.
- Collaborate with data scientists, product managers, and engineers to understand business problems and translate them into ML/AI solutions.
- Optimize model performance through feature engineering, hyperparameter tuning, and algorithm selection.
- Build end-to-end ML pipelines, ensuring seamless integration with production systems.
- Work on large-scale data processing using frameworks such as Spark, Hadoop, or distributed ML libraries.
- Monitor, maintain, and improve deployed models through MLOps best practices(CI/CD, monitoring, retraining).
- Stay up to date with the latest developments in AI/ML research and toolsand apply them to solve real-world problems.
Requirements:
- Strong programming skills in Python(knowledge of Java/Scala/C++ is a plus).
- Expertise in machine learning frameworks(TensorFlow, PyTorch, Scikit-learn, XGBoost, etc. ).
- Solid understanding of statistics, probability, and linear algebra.
- Hands-on experience with data preprocessing, feature engineering, and model deployment.
- Experience with cloud platforms(AWS, GCP, Azure) and ML services.
- Proficiency in working with SQL and NoSQL databases.
- Knowledge of MLOps tools(MLflow, Kubeflow, Airflow, Docker, Kubernetes).