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Senior, niche ML architect reduces competition despite being in a metro location.
Core ML systems and MLOps skills are transferable across industries but require domain-specific expertise.
Explicit 10+ years, mandatory ML architect experience, and specific MLOps/cloud stack required.
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Architect and deliver end-to-end machine learning solutions including data ingestion, feature engineering, model training, deployment, and monitoring.
Lead code reviews and maintain engineering quality standards across ML pipelines and deployment scripts.
Lead technical discussions with clients, mentor ML engineers and data scientists, and support scoping and proposal efforts for ML projects.
Overall 10+ years of experience with 6+ years in machine learning/data science and 3+ years in an architect or technical lead role.
Hands-on proficiency in Python and production ML frameworks such as LightGBM, XGBoost, scikit-learn, PyTorch, or TensorFlow.
Experience deploying production ML models, conducting code reviews across ML pipelines, and implementing MLOps practices including model versioning and monitoring.
Cloud platform experience (Azure, AWS, or GCP), strong SQL skills, and familiarity with version control systems (Git or SVN).
Experienced in translating complex client problems into scalable ML solutions and leading delivery in agile environments with scrum practices.
Strong hands-on technical leader comfortable with end-to-end ML lifecycle and production challenges including model degradation and data quality issues.
Background in client-facing consulting or services delivery with ability to engage in pre-sales activities and technical workshops.