Machine Learning Engineer

• Develop and maintain machine learning pipelines supporting forecasting and predictive analytics use cases.

• 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.

• 4+ years of experience in Machine Learning Engineering, Software Engineering, Data Engineering, or a related field.

• 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.

• 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.