Machine Learning Engineer

We are looking for a skilled Machine Learning Engineer to design, develop, deploy, and monitor production-ready machine-learning solutions. The role involves transforming experimental models and data-driven ideas into scalable, reliable, and maintainable systems that can support real-world product and business requirements.

The candidate will be responsible for preparing and analyzing datasets, performing feature engineering, training and evaluating machine-learning models, and optimizing models for accuracy, performance, and scalability. You will develop reproducible training and inference pipelines and work on deploying models into production environments through APIs, batch-processing workflows, or integrated application services.

Strong knowledge of Python, SQL, statistics, and machine-learning concepts is required, along with hands-on experience using frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, or XGBoost. Familiarity with MLflow, Docker, REST APIs, cloud services, model deployment, and MLOps practices is expected.

The role also involves monitoring production models for accuracy, data drift, model drift, latency, reliability, fairness, and operational cost. You will identify model and pipeline issues, improve inference performance, and establish appropriate monitoring and evaluation processes to ensure models continue to perform effectively after deployment.

The candidate will work closely with data engineers, data scientists, software developers, product teams, and other stakeholders to understand business requirements and develop practical machine-learning solutions. You will also contribute to designing scalable ML pipelines, integrating model capabilities into existing applications, and maintaining reliable production workflows.

The ideal candidate should have strong analytical and problem-solving skills, a practical understanding of machine-learning algorithms and evaluation metrics, and an interest in building production-grade ML systems. You will be expected to maintain clear documentation covering experiments, assumptions, model limitations, evaluation results, deployment processes, and performance metrics, while following modern machine-learning engineering and MLOps best practices.