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Medium—specialized MLOps role with mid-level seniority and common cloud/container skillset increases candidate competition.
Medium—MLOps skills transfer across industries, but fintech production SLAs and domain experience increase specificity.
High—explicit minimum experience, mandatory MLOps/domain experience, and specific cloud/container/CI-CD tech requirements.
Own end-to-end monitoring and health upkeep of production ML models including performance tracking, drift detection, and diagnosing deployment/runtime issues.
Design, build, and maintain scalable ML model serving pipelines ensuring high availability and observability using cloud and containerization technologies.
Collaborate cross-functionally to implement and optimize automated CI/CD pipelines, reduce incidents, and maintain SLAs through monitoring and alerting systems.
Bachelor’s degree in Computer Science, Data Science, or related field.
Minimum 2+ years of hands-on experience in production covering MLOps, DevOps, Data Engineering, or Software Engineering.
Strong proficiency in Docker, Kubernetes, Python, CI/CD pipeline implementation, and cloud platforms (e.g., AWS).
Proven experience with model deployment, proactive monitoring, performance tuning, and incident resolution within SLAs.
Experienced in managing ML model lifecycle in production with a focus on operational stability and cost optimization.
Comfortable leading problem management activities such as root cause analysis and post-mortems to prevent recurring issues.
Skilled in documenting and creating technical knowledge bases including runbooks, SOPs, and model cards to ensure knowledge transfer and compliance.