Site Reliability Engineer In Ai Resume Example
Professional ATS-optimized resume template for Site Reliability Engineer In Ai positions
Jane Doe
Senior Site Reliability Engineer – AI & Machine Learning
Email: jane.doe@example.com | Phone: (555) 123-4567 | LinkedIn: linkedin.com/in/janedoe | GitHub: github.com/janedoe
PROFESSIONAL SUMMARY
Innovative and detail-oriented Senior Site Reliability Engineer specializing in AI infrastructure, model deployment, and scalable systems. Over 8 years of experience optimizing AI pipelines, automating critical operations, and enhancing system reliability in fast-paced environments. Adept at deploying large-scale ML models, implementing observability frameworks, and ensuring high availability for AI-driven applications. Passionate about leveraging automation and cutting-edge cloud technologies to enable robust AI solutions.
SKILLS
- **Hard Skills:**
- AI/ML Model Deployment & Optimization
- Cloud Platforms: AWS, GCP, Azure
- Kubernetes & Docker Containerization
- CI/CD Pipelines & Automation (Jenkins, GitLab CI, ArgoCD)
- Infrastructure as Code (Terraform, Pulumi)
- Monitoring & Observability (Prometheus, Grafana, Datadog, ELK Stack)
- Distributed Systems & Microservices Architecture
- Data Pipeline Orchestration (Apache Airflow, Kubeflow)
- SLO/SLA Management & Incident Response
- **Soft Skills:**
- Strong analytical and problem-solving abilities
- Cross-functional collaboration with Data Science and Engineering teams
- Effective communicator for technical and executive stakeholders
- Continuous Improvement mindset
- Agile methodologies and DevOps culture adoption
WORK EXPERIENCE
*Senior Site Reliability Engineer – AI Infrastructure*
*InnovateAI Labs | San Francisco, CA*
June 2022 – Present
- Led the migration of AI model deployment pipelines to a Kubernetes-based platform, reducing deployment time by 35%.
- Built and maintained scalable data ingest and processing pipelines supporting real-time AI inference workloads using Apache Kafka and Airflow.
- Implemented comprehensive monitoring for ML pipelines, significantly decreasing latency issues and improving system uptime to 99.99%.
- Collaborated with ML teams to optimize resource utilization, resulting in a 20% cost reduction for cloud infrastructure.
- Developed automated incident response scripts and runbooks, accelerating resolution times during outages.
*Cloud Operations & SRE Engineer – Machine Learning Platforms*
*DataX Solutions | New York, NY*
March 2018 – May 2022
- Architected end-to-end deployment solutions for ML models with Kubernetes, Docker, and Terraform, ensuring repeatability and security.
- Maintained high availability of AI services, managing autoscaling policies for fluctuating workloads with GCP AutoML and Cloud Run.
- Established alerting and dashboarding using Prometheus and Grafana, increasing proactive issue detection and resolution efficiency.
- Automated onboarding of new models and data pipelines, reducing manual intervention by 40%.
- Supported AI research teams by developing reproducible CI/CD workflows integrated with GitLab and Jenkins.
*Junior Infrastructure Engineer – Data Science Ops*
*FastData Analytics | Boston, MA*
July 2015 – February 2018
- Assisted in deploying and maintaining ML model repositories, ensuring reproducibility and version control.
- Implemented containerization practices to streamline environment setup for data scientists.
- Managed data pipeline workflows and supported model validation processes across cloud environments.
EDUCATION
**Bachelor of Science in Computer Science**
Massachusetts Institute of Technology (MIT)
*2011 – 2015*
CERTIFICATIONS
- Certified Kubernetes Administrator (CKA) – 2023
- Google Cloud Professional Data Engineer – 2022
- DevOps Foundations (AWS Certified DevOps Engineer – prelims) – 2021
PROJECTS
- **Real-Time AI Monitoring Platform:** Developed a custom observability platform leveraging Prometheus, Grafana, and machine learning anomaly detection models to predict system failures before incidents occurred.
- **Automated Model Deployment Pipeline:** Led a project to build a CI/CD framework automating deployment, rollback, and versioning of ML models, reducing manual steps by 60%.
- **Scalable Data Ingestion System:** Designed a streaming data architecture with Kafka, Spark, and Flink, supporting real-time analytics for NLP applications with 99.999% uptime.
TOOLS & TECHNOLOGIES
- Kubernetes, Docker, Helm
- Terraform, Pulumi
- Prometheus, Grafana, Datadog, ELK Stack
- Apache Kafka, Spark, Flink
- ML Workflow Orchestration: Kubeflow, Airflow
- CI/CD: Jenkins, GitLab CI, ArgoCD
- Cloud Platforms: AWS (SageMaker, EKS, Lambda), GCP (Vertex AI, Cloud Composer), Azure (ML Studio)
LANGUAGES
- Python (Advanced, ML & Automation)
- Bash & PowerShell
- SQL & NoSQL (BigQuery, DynamoDB)
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