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Metro location and mid-level generalist demand balanced by niche MLOps and banking compliance requirements.
Technical SRE and MLOps skills are transferable, but banking regulatory and model-risk knowledge increases domain sensitivity.
Mandatory production support/SRE skills, cloud/MLOps stack and regulated banking compliance increase candidate filtering.
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Provide L2 and L3 production support for AI/ML models and data pipelines in banking systems, ensuring operational availability and compliance.
Monitor AI model performance, detect model drift, manage incident resolution, and support secure deployment and rollback of models.
Implement monitoring, alerting, audit logging, disaster recovery, and business continuity plans to meet regulatory and risk management standards.
Experience in production support, Site Reliability Engineering, or platform engineering, preferably in banking or financial services.
Strong knowledge of AI/ML lifecycle, MLOps practices, and AWS cloud services including SageMaker, EC2, EKS, Lambda, S3, CloudWatch.
Proficiency in Python and scripting; experience with Docker, Kubernetes, microservices, and monitoring tools like CloudWatch, Grafana, Prometheus.
Familiarity with incident management (e.g., ServiceNow), model risk management, data governance, and regulatory requirements (e.g., GDPR).
Experienced in regulated banking environments supporting customer-facing and compliance-related AI workloads with operational resilience focus.
Capable of cross-team collaboration with Data Science, Engineering, Risk, and Compliance teams to manage model lifecycle and risk.
Skilled in leveraging DevOps and automation to optimize AI system performance, security, and cost efficiency.