Software Engineer (MLOps)

Security represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end to end, simplified solutions. The Microsoft Security organization accelerates Microsoft’s mission and bold ambitions to ensure that our company and industry is securing digital technology platforms, devices, and clouds in our customers’ heterogeneous environments, as well as ensuring the security of our own internal estate.

Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to bring their best each day. In doing so, we create life-changing innovations that impact billions of lives around the world. If you are passionate about cybersecurity, data engineering, and MLOps, the M365 Security Engineering team at Microsoft offers an exciting opportunity to work on advanced solutions that safeguard Microsoft services against evolving cyber threats.

Our team is dedicated to building scalable, reliable, and secure data and ML infrastructure that powers mission-critical AI-driven detections across Microsoft’s cloud ecosystem. We value diversity, deep collaboration, and technical excellence, bringing together engineers with expertise in large-scale software systems, security analysis, big data, and machine learning. You will join a group that thrives on analyzing billions of events and terabytes of data generated daily by Microsoft products and services (e.g., Azure, M365), uncovering evidence of suspicious activities and continuously improving detection capabilities. As part of our team, you will help ensure that critical security components and detection pipelines are present, robust, and up to date throughout the infrastructure. You will collaborate with Data Scientists, Security Researchers, and Platform Engineers to deliver seamless, secure, and innovative data workflows, driving the future of AI-powered cybersecurity at Microsoft.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

• 5-8 years' experience in a data infrastructure, DevOps, SRE, or MLOps role supporting high-volume, low-latency data systems.
• Hands-on experience with data architectures, ETL pipelines, and feature engineering for ML workflows Expertise in data mining, data storage, and Extract-Transform-Load (ETL) processes.
• Experience with DevOps practices, CI/CD pipelines, and infrastructure automation using tools like Bicep, Terraform, ARM, or similar.
• Familiarity with distributed systems, containerization (Kubernetes, Docker), and orchestration frameworks.
• Solid understanding of scripting language preferably Python, Scala and pyspark is more suited.
• Familiarity with Azure ML/Data Analytics tools such as Synapse
• Experience with DevOps practices and managing CI/CD pipelines.
• Experience with MLOps tooling for model governance, monitoring, and compliance.

• Bachelor's degree in computer science, Mathematics, or a related field.
• 5-8 years' experience in a data infrastructure, DevOps, SRE, or MLOps role supporting high-volume, low-latency data systems.
• 5-8 years' experience managing and scaling distributed systems, from bare metal to Kubernetes, including deep knowledge across the full stack.
• 5+ years building and deploying containerized applications with Kubernetes and Helm/Kustomize.
• Proficiency in scripting and automation using languages such as Python, Bash, or PowerShell with proven experience in automating operational tasks, including health checks, alerting, and observability for data and ML systems.
• Demonstrated success in troubleshooting and supporting critical production systems with managing CI/CD pipelines and release automation.

• Collaborate with Data Scientists and Security Researchers to extract, transform, and load (ETL) data for exploratory analysis and ML experiments.
• Build and maintain scalable feature engineering pipelines, leveraging POC code and stakeholder inputs.
• Ensure data quality, integrity, and readiness for ML model development.
• Develop robust, scalable, and maintainable ML training and inference pipelines.
• Standardize components using SDKs/frameworks for reusability and consistency.
• Implement model monitoring (accuracy, false positives, etc.) and enable continuous improvement.
• Conduct performance, scalability, and reliability testing of ML pipelines.
• Automate infrastructure provisioning (e.g., using Bicep, Terraform, ARM) and manage CI/CD pipelines for ML workflows.
• Deploy ML pipelines to staging and production environments, provisioning dependencies (linked services, storage, Spark pools/clusters).
• Implement service monitoring, incident response, and operational health checks for data and ML systems.
• Develop self-service tooling to streamline productivity for developers and researchers.
• Work closely with AI researchers, platform engineers, and application developers to deliver seamless, secure data workflows.