We are seeking for AI Quality & Reliability Engineer to own the production readiness of cloud-based AI solutions.
This hybrid role combines automated software testing, AI model evaluation, and Site Reliability Engineering (SRE). The role focuses on building automated frameworks to validate AI outputs while ensuring Azure/AWS infrastructure is resilient, performant, and aligned with banking standards.
Key Responsibilities:
AI Quality & Automated Testing
- Design and execute automated testing frameworks for AI services such as Azure OpenAI and AWS Bedrock.
- Test AI solutions for:
- Model hallucinations
- Accuracy
- Content safety
- Latency
- Build CI/CD-integrated test suites using Python to validate application logic and infrastructure state.
Reliability & Performance
- Implement Chaos Engineering and load testing for banking-scale web and mobile backends.
- Develop automated recovery scripts to support high availability.
- Define and monitor SLIs and SLOs.
- Configure advanced monitoring and alerting using Azure Monitor or AWS CloudWatch.
Security & Compliance
- Automate security scans and compliance checks.
- Ensure AI data handling aligns with banking data residency and privacy requirements.
- Identify inefficient cloud resource usage and expensive failing tests during testing.
Required Skills & Experience:
- 7+ years of relevant experience.
- High proficiency in Python and automation frameworks such as:
- PyTest
- Selenium
- Robot Framework
- Solid hands-on experience with Azure or AWS, particularly networking, scaling, and serverless reliability.
- Understanding of Prompt Engineering and AI output evaluation, including:
- Experience with Grafana, Prometheus, Azure Monitor, AWS CloudWatch, or similar monitoring tools.
- Experience with Bash, GitHub Actions, Terraform, K6/JMeter.
- Exposure to AI evaluation tools such as DeepEval, Ragas, or LangSmith.