Devops Engineer In Ai Resume Example
Professional ATS-optimized resume template for Devops Engineer In Ai positions
John Doe
Hard Skills:
Email: example@email.com | Phone: (123) 456-7890
PROFESSIONAL SUMMARY
Innovative and detail-oriented DevOps Engineer specializing in AI/ML infrastructure deployment and automation. Over 7 years of experience designing scalable CI/CD pipelines, managing cloud-native solutions, and optimizing AI model deployment workflows. Adept at leveraging containerization, orchestration, and infrastructure-as-code (IaC) practices to enable rapid experimentation and reliable production environments for complex machine learning systems. Passionate about driving efficiency, security, and automation in AI development pipelines.
SKILLS
- Cloud Platforms: AWS (SageMaker, EC2, S3, Lambda), GCP (Vertex AI, Cloud Run), Azure (ML Studio, AKS)
- Containerization & Orchestration: Docker, Kubernetes, OpenShift
- CI/CD Pipelines: Jenkins, GitLab CI, CircleCI, Argo CD
- Infrastructure as Code: Terraform, CloudFormation, Pulumi
- Automation & Scripting: Bash, Python, Ansible, Helm
- Monitoring & Logging: Prometheus, Grafana, ELK Stack, Datadog
- Machine Learning Deployment: TensorFlow Serving, TorchServe, Triton Inference Server
- Security & Compliance: Vault, AWS IAM, Azure AD, Role-Based Access Control
**Soft Skills:**
- Cross-functional Collaboration
- Problem-Solving & Debugging
- Continuous Improvement Mindset
- Agile & DevOps Culture Advocacy
- Strong Communication & Documentation
WORK EXPERIENCE
*Senior DevOps Engineer | InnovAI Technologies, San Francisco, CA*
June 2022 – Present
- Designed and implemented a cross-cloud ML pipeline architecture, reducing model deployment time by 35%.
- Automated model versioning, validation, and deployment using GitOps practices with Argo CD and Terraform, ensuring zero-downtime releases.
- Streamlined data ingestion workflows by orchestrating Apache Airflow pipelines, enabling faster experimentation.
- Monitored AI workloads using Prometheus & Grafana, achieving 99.9% uptime and improved alerting accuracy.
- Integrated dynamic scaling for GPU instances, optimizing utilization and reducing cloud costs by 20%.
*DevOps Engineer | AI Horizons, Mountain View, CA*
August 2018 – May 2022
- Led migration of AI workloads to Kubernetes clusters on GCP, enabling high availability and easier rollback procedures.
- Developed CI/CD pipelines with Jenkins & Docker for ML model training and inference deployment, reducing manual errors.
- Established IaC practices using Terraform, ensuring consistent infrastructure provisioning across environments.
- Collaborated with data scientists to containerize and deploy deep learning models with TensorFlow Serving, improving deployment automation.
- Implemented security best practices, including secrets management with HashiCorp Vault, safeguarding sensitive data.
*Junior DevOps Engineer | DataMind Solutions, Austin, TX*
June 2016 – July 2018
- Assisted in building CI/CD pipelines for analytics data pipelines and AI prototypes.
- Managed cloud resources and optimized cluster configurations to support ML workloads.
- Developed Bash and Python scripts to automate routine infrastructure upgrades and backups.
EDUCATION
**Bachelor of Science in Computer Science**
University of Texas at Austin, TX
Graduated: 2016
CERTIFICATIONS
- Certified Kubernetes Administrator (CKA) – 2023
- AWS Certified Solutions Architect – Associate – 2022
- Google Cloud Professional Machine Learning Engineer – 2023
- HashiCorp Certified: Terraform Associate – 2022
PROJECTS
**AI Model Deployment Framework**
Built an automated, scalable deployment framework using Kubernetes, Helm, and Triton Inference Server, allowing data scientists to push models into production with minimal manual intervention. Reduced deployment time from hours to minutes.
**Cost-Efficient GPU Orchestration**
Developed a dynamic scaling solution on AWS leveraging Spot Instances and auto-scaling groups to run AI inference workloads, yielding a 25% reduction in cloud spending.
**Secure ML Infrastructure**
Implemented secrets management and role-based access control policies that ensured compliance with enterprise security standards, preventing unauthorized access to sensitive models and data.
TOOLS & TECHNOLOGIES
- Cloud: AWS, GCP, Azure
- Container & Orchestration: Docker, Kubernetes, Helm
- CI/CD: Jenkins, GitLab CI, Argo CD, CircleCI
- Infrastructure: Terraform, CloudFormation, Pulumi
- Monitoring: Prometheus, Grafana, ELK Stack, Datadog
- Machine Learning: TensorFlow, Torch, Triton Server
LANGUAGES
- Python (fluent)
- Bash (advanced)
- YAML & JSON (expert)
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