Machine Learning

About The Opportunity

• Design and implement end-to-end ML pipelines: data ingestion, preprocessing, training, deployment, and monitoring.
• Develop and fine-tune machine learning models (classification, regression, clustering) using Python frameworks like scikit-learn, TensorFlow, or PyTorch.
• Collaborate cross-functionally with Data Engineers, Data Scientists, and Product teams to translate business requirements into scalable ML solutions.
• Deploy and manage models in production on cloud platforms (AWS/Azure/GCP) leveraging containerization (Docker) and orchestration (Kubernetes).
• Monitor model performance, detect drift, and automate retraining workflows to maintain accuracy and reliability.
• Maintain code quality and reproducibility: version control (Git), CI/CD pipelines, documentation, and best practices for ML development.
  

Skills & Qualifications

• Bachelor’s/Master’s in Computer Science, Engineering, Mathematics, or related field.
• 2+ years hands-on experience building and deploying ML models in production.
• Proficiency in Python and core ML libraries: scikit-learn, TensorFlow, PyTorch.
• Strong understanding of statistics, feature engineering, and model evaluation techniques.
• Experience with SQL/NoSQL databases and data processing tools (Pandas, Spark).
• Familiarity with cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform) and containerization (Docker).
  

Preferred

• Hands-on with Kubernetes or other orchestration tools for scalable deployments.
• Experience in NLP, computer vision, or time-series forecasting projects.
• Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow) for automated pipelines.
• Exposure to model interpretability methods (SHAP, LIME) and A/B testing in production.
  

• Competitive compensation with performance-linked bonuses.
• Collaborative, inclusive on-site culture fostering continuous learning and innovation.
• Regular team workshops, hackathons, and professional development support.