Sr. Engineer, Cloud Native - AI Detection and Response (AIDR) (Hybrid, Sunnyvale)
$140,000–$215,000 year
HybridSunnyvale, California, United States
Sunnyvale, California, United StatesHybridFull Time$140,000–$215,000 yearSenior LevelEnterprise
Full TimeSenior LevelEnterprise
Job Summary
Architect and build scalable microservices and platform components that process millions of AI security events per second. Develop comprehensive tests for reliability, accuracy, and performance, while conducting code reviews focused on testability, security, and scalability. Integrate seamlessly with the CrowdStrike ecosystem and drive root cause analysis for production issues to implement preventive measures. Mentor team members on secure coding practices and cloud-native development methodologies. This role requires 10+ years of software development experience and proficiency in cloud-native languages like Go, Python, or Java. The position is hybrid, requiring 2-3 days per week on-site in Sunnyvale.
Required Qualifications
- 10+ years of software development experience in building Enterprise grade products
- 5+ years of experience in developing microservices that run in cloud native environments
- Strong computer science fundamentals (algorithms, data structures, distributed systems)
- Expertise in at least one cloud native programming language: GO, Python, Java
- Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes
- Deep understanding of: Cloud architectures and microservices
- Deep understanding of Web Services: REST, gRPC, Protocol Buffers or similar API technologies
- Deep understanding of Data storage systems: PostgreSQL, Redis or similar databases
- Deep understanding of Container technologies: Docker, Kubernetes or similar orchestration platforms
- Proven track record of deploying services to at least one of cloud environments (AWS/OCI/GCP/Azure)
- Strong debugging skills with ability to troubleshoot complex distributed systems
- Experience with cloud monitoring, profiling and optimization tools
- Experience implementing CI/CD pipelines for ML models and automated retraining workflows
- Knowledge of model versioning, experiment tracking, and feature store implementations
- Experience with A/B testing and canary deployments for ML models in production
- Familiarity with model explainability and interpretability tools (SHAP, LIME, etc.)
- Experience with ML observability platforms and model performance monitoring in production
- Experience with real-time data processing systems (Kafka, Pulsar, Splunk)
- Hands-on experience with working in cloud environments such as Oracle Cloud Infrastructure (OCI), Google Cloud Platform (GCP) and/or Microsoft Azure
- Golang experience
- MS CoPilot Ecosystem and MS Agent Studio
- Highly scalable and performant Cloud Native Service experience on AWS
- MLOps experience including model deployment, monitoring, and lifecycle management
- Experience with ML model evaluation frameworks and metrics (precision, recall, F1, AUC-ROC, drift detection)
- Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML
- Experience implementing CI/CD pipelines for ML models and automated retraining workflows
- Knowledge of model versioning, experiment tracking, and feature store implementations
- Experience with A/B testing and canary deployments for ML models in production
- Familiarity with model explainability and interpretability tools (SHAP, LIME, etc.)
- Experience with ML observability platforms and model performance monitoring in production
- Previous experience in cybersecurity companies or security-focused products
- Deep understanding of AI/ML security challenges, threats and mitigation strategies
- Experience with real-time data processing systems (Kafka, Pulsar, Splunk)
- Contributions to open source libraries and/or frameworks
- Hands-on experience with working in cloud environments such as Oracle Cloud Infrastructure (OCI), Google Cloud Platform (GCP) and/or Microsoft Azure
- GCP, OCI & Kubernetes AWS and API Gateway
- Golang experience
- MS CoPilot Ecosystem and MS Agent Studio
- Highly scalable and performant Cloud Native Service experience on AWS
- MLOps experience including model deployment, monitoring, and lifecycle management
- Experience with ML model evaluation frameworks and metrics (precision, recall, F1, AUC-ROC, drift detection)
- Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML
- Experience implementing CI/CD pipelines for ML models and automated retraining workflows
- Knowledge of model versioning, experiment tracking, and feature store implementations
- Experience with A/B testing and canary deployments for ML models in production
- Familiarity with model explainability and interpretability tools (SHAP, LIME, etc.)
- Experience with ML observability platforms and model performance monitoring in production
- 2-3 days per week on-site at one of the posted locations
Desired Qualifications
- Previous experience in cybersecurity companies or security-focused products
- Deep understanding of AI/ML security challenges, threats and mitigation strategies
- Experience with real-time data processing systems (Kafka, Pulsar, Splunk)
- Contributions to open source libraries and/or frameworks
- Hands-on experience with working in cloud environments such as Oracle Cloud Infrastructure (OCI), Google Cloud Platform (GCP) and/or Microsoft Azure
- GCP, OCI & Kubernetes AWS and API Gateway
- Golang experience
- MS CoPilot Ecosystem and MS Agent Studio
- Highly scalable and performant Cloud Native Service experience on AWS
- MLOps experience including model deployment, monitoring, and lifecycle management
- Experience with ML model evaluation frameworks and metrics (precision, recall, F1, AUC-ROC, drift detection)
- Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML
- Experience implementing CI/CD pipelines for ML models and automated retraining workflows
- Knowledge of model versioning, experiment tracking, and feature store implementations
- Experience with A/B testing and canary deployments for ML models in production
- Familiarity with model explainability and interpretability tools (SHAP, LIME, etc.)
- Experience with ML observability platforms and model performance monitoring in production
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