Machine Learning Engineer

About the role

The Machine Learning Engineer is responsible for designing, building, deploying, and maintaining production‑grade machine learning solutions that drive business value across the enterprise. This role sits at the intersection of software engineering and data science, with a strong focus on scalable ML systems, model lifecycle management, and integration with enterprise platforms. The ideal candidate is hands‑on, technically strong, and comfortable operating in a fast‑paced, cross‑functional environment. Key responsibilities include: Design, develop, train, and deploy machine learning models using supervised and unsupervised techniques (e.g., regression, classification, clustering, anomaly detection). Build and maintain end‑to‑end ML pipelines, including data ingestion, feature engineering, training, evaluation, and inference. Partner with Data Science, Data Engineering, and business stakeholders to translate requirements into scalable technical solutions. Implement MLOps best practices, including CI/CD, model versioning, monitoring, and retraining strategies. Optimize model performance, scalability, reliability, and cost efficiency in production environments. Integrate machine learning models into enterprise applications, APIs, and data platforms. Ensure data quality, model explainability, and adherence to security, governance, and compliance standards. Communicate complex machine learning concepts and results clearly to technical and non‑technical stakeholders.