Site Reliability Engineer (SRE)- Banking Industry
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Key skills for this role
About the Role
Fecundity Technologies seeks a Site Reliability Engineer to ensure production readiness of cloud-based AI solutions for banking. The role combines automated testing and SRE to build resilient Azure/AWS infrastructure, implement chaos engineering, and define SLIs/SLOs.
Key Skills for This Role
Responsibilities
- Implement Chaos Engineering and load testing to ensure web/mobile backends can handle banking scale traffic
- Build CI/CD integrated test suites using Python that validate both application logic and infrastructure state
- Define and monitor Service Level Indicators (SLIs) and Objectives (SLOs)
- Set up advanced alerting in Azure Monitor or AWS CloudWatch to catch performance degradation
- Automate security scans and compliance checks to ensure AI data handling meets banking data residency and privacy protocols
Requirements
- High proficiency in Python and framework automation (PyTest, Selenium, or Robot Framework)
- Strong hands on experience with Azure or AWS, regarding networking, scaling, and serverless reliability
- Understanding of Prompt Engineering and how to evaluate AI model outputs
- Experience with monitoring tools like Grafana, Prometheus, or native cloud monitoring tools
- Ability to identify expensive failing tests or inefficient cloud resource usage
- Site Reliability Engineering or Production Support: 4 years (Preferred)
- Python: 4 years (Preferred)
Full Job Posting
Role Overview
- We are seeking a Site Reliability Engineer to own the 'Production Readiness' of our cloud based AI solutions. This hybrid role combines automated software testing and Site Reliability Engineering (SRE). You will build the automated frameworks that validate our AI outputs and ensure the underlying Az
Key Responsibilities
- Resiliency Engineering (SRE): Implement Chaos Engineering and load testing to ensure web/mobile backends can handle banking scale traffic. Maintain high availability through automated recovery scripts.
- Automated Regression: Build CI/CD integrated test suites using Python that validate both the application logic and the infrastructure state (IaC validation).
- Observability & SLIs: Define and monitor Service Level Indicators (SLIs) and Objectives (SLOs). Set up advanced alerting in Azure Monitor or AWS CloudWatch to catch performance degradation before users do.
- Security & Compliance Testing: Automate security scans and compliance checks to ensure all AI data handling meets strict banking data residency and privacy protocols.
Technical & Professional Requirements
- Automation Stack: High proficiency in Python (for AI testing) and framework automation (PyTest, Selenium, or Robot Framework).
- Cloud Infrastructure: Strong hands on experience with Azure or AWS, specifically regarding networking, scaling, and serverless reliability.
- AI/ML Understanding: Understanding of Prompt Engineering and how to evaluate AI model outputs (RAG evaluation, ROUGE/BLEU scores, or custom LLM benchmarks).
- Monitoring Tools: Experience with Grafana, Prometheus, or native cloud monitoring tools to build real time reliability dashboards.
- FinOps Awareness: Ability to identify 'expensive' failing tests or inefficient cloud resource usage during the testing phase.
Recommended Skillset & Tools
- Languages: Python (Mandatory), Bash scripting.
- Tools: GitHub Actions (CI/CD), Terraform (reading/validating), K6 or JMeter (Performance).
- AI Frameworks: DeepEval, Ragas, or LangSmith (for automated AI evaluation).
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