Machine Learning Engineer

Career Category Engineering Job Description ABOUT THE ROLE We are seeking a Machine Learning Engineer, Forecasting to join the Forecasting team within the AI & Data organization. This role will build, deploy, and maintain machine learning systems that support forecasting and decision-making across the organization. Working closely with data scientists, data engineers, and business stakeholders, the Machine Learning Engineer will help translate forecasting models into reliable production systems while contributing to scalable ML infrastructure, data pipelines, and MLOps capabilities. The ideal candidate is passionate about software engineering, machine learning, and building production-grade AI solutions Key Responsibilities Develop and maintain machine learning pipelines supporting forecasting and predictive analytics use cases. Productionize statistical, machine learning, and Bayesian forecasting models developed by data scientists. Build data ingestion, preprocessing, feature engineering, and model inference pipelines using structured and unstructured data. Deploy ML models into production environments and support model lifecycle management. Implement model monitoring, automated retraining, performance tracking, and alerting mechanisms. Collaborate with data scientists to optimize model performance, scalability, and reliability. Build reusable ML components, APIs, and services following software engineering best practices. Contribute to CI/CD pipelines and MLOps workflows for automated testing and deployment. Troubleshoot production issues, optimize system performance, and improve operational efficiency. Document technical solutions and contribute to engineering best practices across the team. Stay current with emerging machine learning engineering technologies and recommend improvements where appropriate. Basic Qualifications 4+ years of experience in Machine Learning Engineering, Software Engineering, Data Engineering, or a related field. Experience building and deploying production machine learning applications. Strong programming skills in Python and SQL. Experience with machine learning libraries such as scikit-learn, PyTorch, TensorFlow, or similar frameworks. Experience building data pipelines and feature engineering workflows. Familiarity with MLOps concepts including ML lifecycle management, CI/CD, model versioning, and monitoring. Experience with cloud platforms such as Azure, AWS, or GCP. Familiarity with containerization technologies such as Docker and orchestration platforms like Kubernetes. Experience working with distributed data processing frameworks such as Spark or Databricks. Understanding of software engineering best practices including testing, version control, code reviews, and documentation. Strong analytical, problem-solving, and communication skills. up. Preferred Qualifications Experience supporting forecasting or time-series machine learning applications. Familiarity with Bayesian or probabilistic forecasting models. Experience deploying ML solutions in healthcare, biotech, retail, supply chain, or commercial analytics domains. Experience with MLFlow, Airflow, Azure ML, Databricks, or similar ML platforms. Familiarity with model explainability and monitoring tools such as SHAP, Evidently, or equivalent. Exposure to modern AI technologies including Generative AI and LLM-based applications is a plus. Ability to work effectively in cross-functional Agile teams. .