Senior AI Data Platform Reliability & Validation Engineer 3

Responsibilities

Key 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)

5+ 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.oling / infrastructure