Overview

We are looking for an AI Production Support Engineer to support and operate AI and ML solutions within a regulated banking environment. In this role, you will help ensure the availability, resilience, compliance, and risk management of AI systems that support critical banking services.
Project Overview:

Responsibilities:
  • Provide L2 and L3 production support for AI and ML models and data pipelines used in banking systems
  • Monitor model performance, model drift, data quality, and the operational health of AI services
  • Ensure the stability and availability 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 the 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, including GDPR and model risk management frameworks
  • Support disaster recovery (DR) and business continuity planning (BCP) for AI workloads
  • Identify opportunities for automation, operational efficiency, and cost optimization
Required Qualifications:
  • Experience in production support, Site Reliability Engineering (SRE), or platform engineering, preferably within banking or financial services
  • Strong understanding of the AI and ML lifecycle and MLOps practices
  • Experience with AWS cloud services and secure cloud workloads
  • Proficiency in Python and scripting for troubleshooting, debugging, and automation
  • Hands on experience with Docker, Kubernetes, and microservices architectures
  • Familiarity with MLOps platforms and tools such as MLflow and Amazon SageMaker
  • Experience with monitoring and observability tools including Amazon CloudWatch, Splunk, Grafana, and Prometheus
  • Knowledge of data pipelines, APIs, and both batch and real time processing systems
  • Experience with incident management platforms such as ServiceNow
  • Understanding of Model Risk Management (MRM) and audit requirements
  • Knowledge of data governance, data lineage, and control frameworks
Nice To Have:
  • Exposure to AI governance frameworks and model explainability tools
  • Experience supporting fraud detection, credit risk, or financial analytics models
  • Knowledge of DevSecOps practices and secure software delivery
  • Relevant AWS, MLOps, or cloud related certifications
Note:

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