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.
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