לוח משרות דרושים QA Automation infrastracture בתל אביב יפו, לחיפוש זה נמצאו 5 משרות

Ww are looking for an Experienced QA Engineer to write test scenarios, automating test flows and execute manual test plans for web applications! We are looking for people who look at the world differently, people who are passionate about making sense out of huge amounts of data, who explore, analyze and lead the solutions they envision. If you are an engineer that is passionate about being part of an innovative product, making an impact, and striving to work with the industrys best talent as part of one of the fastest growing and successful startups around - this is where you want to be!
So what will you actually be doing?
Design, plan, analyze QA tests/test-plans for web applications.
Work closely with the development teams and be part of a development cycle.
Design and execute automated end-to-end regression and sanity tests, identifying gaps and deficiencies.
Assess delivery readiness and maintain outstanding UX/UI standards through manual testing as needed.
Investigate product quality issues to uphold high-quality standards.
Collaborate with Product and other teams to manage documentation and ensure timely releases.
Review final product functionality before releases.
Assist and guide developers in creating test plans for their new features.

We are looking for a QA Data Automation Engineer to join our Data Team in a dynamic and challenging role, providing critical test coverage for our data pipelines, reporting layers, and analytics solutions.
In this role, you will be responsible for validating data integrity end-to-end - from raw ingestion and transformation layers to dashboards and downstream consumers. You will design and maintain automated tests to ensure accurate, reliable, and scalable data systems.
Key Responsibilities:
Develop and maintain automated QA tests for data pipelines, transformations, and data products.
Perform and execute manual QA testing such as functional, regression, and sanity testing of applications, dashboards, and backend systems.
Validate data flow across the system, including: ingestion, transformations, reports/dashboards.
Perform data quality testing (completeness, consistency, accuracy, timeliness, schema validation).
Write and execute SQL-based tests to validate logic, joins, aggregations, metrics, and anomalies.
Build automation frameworks and validation scripts using Python.
Work closely with Data Engineers and Analytics/BI stakeholders to define test coverage and acceptance criteria.
Investigate failures and data issues, providing clear RCA and actionable bug reports.
Document test plans, test scenarios, expected results, and automation coverage.
Track issues in Jira, including reproducible steps and supporting evidence.
Continuously improve QA processes for better monitoring, reliability, and faster releases.

Required Al Infrastructure & Reliability Engineer
What this role is really about
Youll join a 3-person platform team within our Business Technology group -owning the internal infrastructure that our AI platform and its users depend on. This isnt a product engineering role, and it isnt ticket work or babysitting pipelines someone else built. Youre building and operating the internal foundation that the company runs on. The work covers the full stack of platform engineering: core cloud infrastructure (AWS, Kubernetes, IaC), CI/CD pipelines, AI-driven infrastructure components, and the SRE and observability practice that keeps it all honest -metrics, alerting, incident response, and reliability standards. As our AI capabilities grow, so does the complexity underneath them, and staying ahead of that is central to the role. If you treat infrastructure as a product -reusable, automated, observable, and built to last -this is your kind of role.
Job responsibilities
DevOps & AI-Driven Infrastructure - own CI/CD, deployment processes, and release reliability. Build and operate cloud infrastructure that is automated, intelligent, and continuously self-improving - not just managed.
Design and build our Terraform repository and IaC pipeline from scratch -AI-assisted generation, drift detection, and policy enforcement built in.
Build AI-driven GitHub Actions pipelines -automated code review, risk assessment, and intelligent deployment decisions.
Manage Kubernetes workloads across AWS accounts -zero downtime, fully automated, nothing left behind.
Embed AI into the operational layer -proactive drift detection, automated remediation, and intelligent scaling toward a self-healing runtime.
Reliability & SRE -improve uptime, resilience, and incident response.
Define and enforce SLOs/SLIs, error budgets, and on-call practices.
Lead incident response, postmortems, and systemic reliability improvements.
Own AI-specific reliability: model latency SLOs, token quota monitoring, rate limit handling, fallback and retry strategies, and cost-per-request alerting.
Observability & Telemetry - increase visibility, reduce noise, improve troubleshooting.
Establish and continuously evolve the observability stack: metrics, logs, distributed tracing, and alerting tuned for both application and AI workloads.
AI / LLM Operations- bringing AI systems to production and operating them at scale, with a focus on reliability, performance, and trust.
Own the AI infrastructure layer: rate limits, quota management, latency SLOs, and fallback strategies (retries, circuit breakers).
Operate LLM APIs in production with resilience and cost attribution per team/model.