Site Reliability Engineer-AI production-automated testing ,Observability
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Key skills for this role
About the Role
TAT IT Technologies is urgently hiring a Site Reliability Engineer for a banking client in Abu Dhabi. This hybrid role combines SRE and automated testing to ensure AI-driven cloud applications are production-ready, resilient, and compliant with banking standards.
Key Skills for This Role
Responsibilities
- Design and implement Python based automated testing frameworks to validate AI application logic, APIs, and cloud infrastructure.
- Conduct chaos testing, load testing, and fault injection to ensure systems withstand failures and maintain service continuity.
- Establish clear Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for AI workloads.
- Build proactive monitoring, alerting, and logging pipelines across Azure and AWS environments.
- Implement automated compliance checks aligned with banking regulations.
- Integrate AI specific evaluation frameworks to assess model performance, fairness, and reliability in production.
Requirements
- Strong expertise in Python based testing frameworks (PyTest, Robot, or similar).
- Experience with Azure / AWS cloud platforms.
- Hands on observability tools (Prometheus, Grafana, ELK, Datadog).
- Experience defining and implementing SLIs/SLOs for distributed systems.
- Practical exposure to chaos engineering and load testing frameworks (Gremlin, Locust, Jmeter).
- Familiarity with AI/ML evaluation tools for production readiness.
- Strong background in security and compliance automation within regulated industries (banking/finance).
Full Job Posting
Role Overview
- We are seeking a Site Reliability Engineer (AI Production Readiness) to ensure our AI driven cloud applications are production ready, resilient, and compliant with banking standards. This hybrid role combines SRE practices with automated testing expertise, focusing on reliability, observability, and
Key Responsibilities
- Automated Validation Frameworks: Design and implement Python based automated testing frameworks to validate AI application logic, APIs, and cloud infrastructure.
- Resilience Engineering: Conduct chaos testing, load testing, and fault injection to ensure systems withstand failures and maintain service continuity.
- SLIs/SLOs Definition: Establish clear Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for AI workloads, ensuring measurable reliability targets.
- Observability & Monitoring: Build proactive monitoring, alerting, and logging pipelines across Azure and AWS environments to detect anomalies before they impact users.
- Security & Compliance: Implement automated compliance checks aligned with banking regulations, ensuring secure deployment pipelines and audit readiness.
- AI Evaluation Tools: Integrate AI specific evaluation frameworks to continuously assess model performance, fairness, and reliability in production.
Must Have Skills
- Strong expertise in Python based testing frameworks (PyTest, Robot, or similar).
- Experience with Azure / AWS cloud platforms.
- Hands on observability tools (Prometheus, Grafana, ELK, Datadog).
- Experience defining and implementing SLIs/SLOs for distributed systems.
- Practical exposure to chaos engineering and load testing frameworks (Gremlin, Locust, Jmeter).
- Familiarity with AI/ML evaluation tools for production readiness.
- Strong background in security and compliance automation within regulated industries (banking/finance).
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