AI Data Platform Reliability

2 weeks ago

New Delhi, India Oracle Full time

Responsibilities- Design, develop, and execute end-to-end (E2E) scenario validations that simulate real-world usage of complex AI data platform workflows (data ingestion, transformation, ML pipeline orchestration, etc.). - Collaborate closely with product, engineering, and field teams to identify gaps in coverage and propose test automation strategies. - Develop and maintain automated test frameworks supporting E2E, integration, performance, and regression testing for distributed data/AI services - Monitor system health across the stack (infrastructure, data pipelines, AI/ML workloads), proactively detect failures or SLA breaches. - Champion SRE best practices including observability, incident management, blameless postmortems, and runbook automation. - Analyze logs, traces, and metrics to identify reliability, latency, and scalability issues; drive root cause analysis and corrective actions. - Partner with engineering to drive high-availability, fault tolerance, and continuous delivery (CI/CD) improvements. - Participate in on-call rotation to support critical services, ensuring rapid resolution and minimizing customer impact.Desired Qualifications:- Bachelor’s or master’s degree in computer science, Engineering, or related field (or demonstrated equivalent experience) - 3+ years’ experience in software QA/validation, SRE, or DevOps roles, ideally in data platforms, cloud, or AI/ML environments. - Proficient with DevOps automation and tools for continuous integration, deployment, and monitoring (e.g., Terraform, Jenkins, GitLab CI/CD, Prometheus). - Working knowledge of distributed systems, data engineering pipelines, and cloud-native architectures (OCI, AWS, Azure, GCP, etc.). - Strong proficiency in Java, Python and related technologies - Hands-on experience with test automation frameworks (e.g., Selenium, pytest, JUnit) and scripting (Python, Bash, etc.). - Familiarity with SRE practices: service-level objectives (SLO/SLA), incident response, observability (Prometheus, Grafana, ELK, etc.). - Strong troubleshooting and analytical skills with a passion for reliability engineering and process automation. - Excellent communication and cross-team collaboration abilities.

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