Machine learning Engineer

Requirements

Machine Learning Engineer

Key Responsibilities

• Design, develop, and deploy machine learning models for predictive analytics, classification, recommendation, and anomaly detection.
• Build scalable data pipelines for feature extraction, model training, and evaluation using modern ML frameworks and tools.
• Collaborate with data scientists to convert research prototypes into production-ready models.
• Implement end-to-end ML systems, from data ingestion and preprocessing to model deployment and monitoring.
• Continuously evaluate and improve model performance through tuning, retraining, and data-driven optimization.
• Work with software and platform teams to integrate ML outputs into core applications and APIs.
• Develop automated CI/CD workflows for ML experiments and production deployments.
• Ensure data and model governance, including version control, reproducibility, and explainability.
• Stay up to date with emerging trends in machine learning, deep learning, and MLOps, bringing innovative ideas to the team.
• Partner with stakeholders to identify business challenges that can be solved with ML-driven approaches.
  

Key Skills And Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field.
• 4+ years of hands-on experience in building and deploying machine learning models in real-world applications.
• Strong knowledge of machine learning algorithms, statistical modeling, and data preprocessing techniques.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
• Expertise in data manipulation and analysis using Pandas, NumPy, and SQL.
• Experience with end-to-end ML pipelines, including data engineering, model training, testing, and deployment.
• Working knowledge of cloud platforms such as AWS, GCP, or Azure for ML workloads.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes is a plus.
• Strong analytical and problem-solving skills with attention to scalability and performance.
• Excellent communication skills and ability to collaborate across technical and business teams.
  

Preferred Qualifications

• Experience in MLOps tools like MLflow, Kubeflow, Airflow, or SageMaker.
• Understanding of deep learning architectures for NLP or computer vision.
• Exposure to feature store management, model versioning, and monitoring frameworks.
• Passion for continuous learning and applying ML to real-world business problems