ML-focused Site Reliability Engineer Developer Platforms

Bucharest - Romania

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Our Company

Changing the world through digital experiences is what Adobes all about. We give everyonefrom emerging artists to global brandseverything they need to design and deliver exceptional digital experiences! Were passionate about empowering people to create beautiful and powerful images videos and apps and transform how companies interact with customers across every screen.

Were on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization and we know the next big idea could be yours!

The Opportunity

We have a fantastic opportunity for a ML-focused Site Reliability Engineer to join our Developer Platforms team based in Bucharest.

We are looking for an engineer with hands-on experience in machine learning including designing and training models for real-world applications. The ideal candidate will play a crucial role in developing and implementing anomaly detection systems to proactively spot and address operational issues in intricate infrastructures. This role demands a strong understanding of AI Ops methodologies to optimize performance automate incident response and enhance system reliability. Candidates should be enthusiastic about using data to drive intelligent automation and improve service resilience at scale.

The Role:

Build outstanding things that matter. Youll work on a critical growth initiative solving problems for engineers and customers.
Grow. Sharpen your skills use innovative technology and collaborate with your peers.
Collaborate. Work in an environment that values collaboration.

What youll do:

Ensure the highest level of uptime and Quality of Service (QoS) to Adobes customers through operational excellence
Architect and build an AI Anomaly-detection system that works on Adobes observability data at scale partnering with other teams to work across boundaries.
Define service level objectives (SLOs) and service level indicators (SLIs) to represent and measure service quality
Identify areas to improve service resiliency through techniques such as chaos engineering performance/load testing anomaly detection etc
Support and maintain globally distributed multi-cloud (public and/or private) environments
Automate common repeatable tasks at a large scale to reduce toil
Tackle performance and stability issues using a wide variety of tools
Participate in an on-call rotation as required
Determine the root cause for all production level incidents and write corresponding high-quality RCA reports

What youll need to succeed:

Hands-on experience with AI anomaly detection and training models
Expert in MCP integration with experience in MCP to MCP communication as a nice to have.
Understanding of how to fine-tune signals from observability systems to allow our AI capabilities to scale for Production data.
Deep understanding of both software engineering and technical operations
DevOps skills (scrum/Kanban/agile/ci-cd/12-factor)
Experience in modern cloud-based SaaS delivery technologies: AWS Azure Jenkins Git Atlassian Jira and Confluence Linux DNS E-mail containers log analysis monitoring Java Apache Tomcat Memcached Qpid and MySQL on Linux Prometheus Grafana New Relic Splunk.
Expertise with containerization orchestration engines (Kubernetes)
Programming skills particularly with Python Java and Ruby
Applied skills in machine learning
Excellent communication interpersonal and teamwork skills
Familiar with a variety of cloud and automation concepts practices and procedures

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Our CompanyChanging the world through digital experiences is what Adobes all about. We give everyonefrom emerging artists to global brandseverything they need to design and deliver exceptional digital experiences! Were passionate about empowering people to create beautiful and powerful images videos...