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Senior specialized ML leadership role with niche MLOps requirements reduces applicant competition.
Requires deep ML production and MLOps expertise, limiting transferability across industries.
Explicit 8+ years requirement plus mandatory hands-on MLOps and production model experience raises filtering strictness.
Lead end-to-end design, development, evaluation, and deployment of production-grade machine learning models addressing predictive, classification, recommendation, anomaly detection, forecasting, and optimization use cases.
Develop and operationalize robust, scalable ML pipelines and MLOps practices ensuring reproducibility, maintainability, and measurable business impact.
Mentor data scientists and ML engineers on modeling rigor, experimentation, production-readiness, and contribute reusable ML assets and best practices across the team.
8+ years experience in data science, machine learning, applied AI, or advanced analytics with proven production ML deployment.
Strong hands-on expertise in Python and ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow, etc.) and end-to-end pipeline development including feature engineering and monitoring.
Practical experience with MLOps tools and platforms such as Azure Machine Learning, Databricks, MLflow, Azure DevOps, Docker, Kubernetes or equivalents.
Experience with integrating ML models into applications/APIs, working knowledge of data engineering patterns including SQL and streaming, plus understanding of model lifecycle management.
Experienced technical leader capable of guiding complex ML projects involving cross-functional teams and multiple models from design through operationalization.
Proficient in advanced MLOps practices and tooling, including experiment tracking, CI/CD for ML, model governance, monitoring, and continuous improvement in a production product environment.
Strong ability to translate business requirements into scalable ML solutions and lead strategic decisions on model architecture, operationalization, and team mentorship.