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Hybrid/remote, mid-level generalist ML role in metro with broad skillset increases competition.
Applied ML engineering and MLOps skills are broadly transferable across industries.
Explicit 2–6 years requirement plus mandatory ML, cloud, and production deployment skills enforces strict filtering.
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Translate business requirements into data science and engineering solutions to generate actionable insights.
Build, train, deploy, and optimize predictive and prescriptive machine learning models in production using MLOps practices.
Develop and maintain data pipelines for batch and real-time data processing supporting business impact.
2–6 years of experience in data science, analytics, or data engineering roles.
Proficiency in Python (Pandas, NumPy, scikit-learn), SQL, and cloud platforms (AWS/GCP/Azure).
Experience in building and deploying ML models to production; knowledge of containerization (Docker).
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, AI, or equivalent experience.
Experienced in end-to-end machine learning lifecycle including data engineering, modeling, and deployment with MLOps.
Comfortable working in a fast-paced environment with focus on measurable business outcomes.
Familiarity with cloud and containerization technologies aligned with hybrid/remote work setups.