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Metro mid-level ML engineering role with broad stack and recognizable employer increases applicant competition.
AI automation, data pipelines, and cloud deployment skills are broadly transferable across industries.
Explicit 5+ years plus many mandatory ML, cloud, and deployment skills increases filtering strictness.
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Automate and refactor Python/PySpark data workflows into production-ready systems in collaboration with data scientists.
Design, implement, and manage scalable AI-driven data pipelines that automate repetitive tasks and enable real-time decision-making.
Build and integrate automation tools including job schedulers, version control, and monitoring solutions to optimize project delivery and workflow efficiency.
5+ years of relevant experience in data science engineering or automation roles.
Proficiency in Python scripting and automation, including modular code design.
Experience with AI automation techniques such as reinforcement learning, decision trees, autonomous or multi-agent systems.
Hands-on experience with workflow orchestration tools (e.g., Apache Airflow), cloud deployment (AWS, GCP, or Azure), version control (Git), Docker, Kubernetes, and API frameworks (FastAPI or Flask).
Experienced in applying AI-driven automation to optimize and scale data science projects and workflows.
Capable of operationalizing data science codebases into maintainable, production-grade systems and tools.
Comfortable working across cloud environments with strong understanding of deployment, orchestration, and monitoring pipelines.