01Overview
The Job
We are looking for an AI PrSRE Engineer to support and operate AI/ML solutions within a regulated banking environment. The role focuses on ensuring high availability, resilience, compliance, and risk management of AI systems that support critical banking services.
Responsibilities
Provide L2/L3 production support for AI/ML models and data pipelines used in banking systems
Monitor model performance, drift, data quality, and operational health of AI services
Ensure stability and uptime of AI platforms supporting customer-facing and regulatory workloads
Perform incident management, root cause analysis (RCA), and problem management in line with ITIL practices
Collaborate with Data Science, Engineering, Risk, and Compliance teams
Support secure deployment, release, and rollback of models in production
Implement monitoring, alerting, and audit logging to meet regulatory and audit requirements
Ensure adherence to data privacy, governance, and financial regulatory standards (e.g., GDPR, model risk frameworks)
Support disaster recovery (DR) and business continuity (BCP) plans for AI workloads
Identify opportunities for automation, operational efficiency, and cost optimization
Your Profile
Requirements
Experience in production support / SRE / platform engineering, preferably in banking or financial services
Strong understanding of AI/ML lifecycle and model operations (MLOps)
Experience with cloud platforms (Azure preferred in banking), including secure workloads
Proficiency in Python and scripting for debugging and automation
Hands-on experience with Docker, Kubernetes, and microservices architectures
Familiarity with MLOps tools (MLflow, Azure ML, SageMaker, etc.)
Experience with monitoring & observability tools (CloudWatch, Splunk, Grafana, Prometheus)
Knowledge of data pipelines, APIs, batch and real-time processing systems
Experience with incident management tools (e.g., ServiceNow)
Understanding of model risk management (MRM) and audit expectations
Awareness of data governance, lineage, and controls
Familiarity with security standards and identity access management (IAM)
Nice to have
Exposure to AI governance frameworks and explainability tools
Experience with fraud detection, credit risk, or financial analytics models
Knowledge of secure DevOps (DevSecOps) practices
Relevant certifications (AWS, MLOps) .