Mlops Engineer In Devops Resume Example
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John Doe
Senior MLOps Engineer | DevOps Specialist
Email: johndoe@email.com | Phone: (123) 456-7890 | LinkedIn: linkedin.com/in/johndoe | Location: New York, NY
PROFESSIONAL SUMMARY
Innovative and detail-oriented Senior MLOps Engineer with over 6 years of experience in deploying, managing, and optimizing machine learning workflows within cloud-native environments. Expertise in designing scalable CI/CD pipelines for ML models, implementing robust monitoring solutions, and fostering collaboration between data science and engineering teams. Passionate about ensuring model reliability and performance at scale, leveraging cutting-edge tools and automation practices aligned with industry standards for 2025.
SKILLS
Hard Skills
- Machine Learning Operations (MLOps)pipeline design
- Cloud platforms: AWS, Azure, GCP
- Containerization & orchestration: Docker, Kubernetes, EKS, AKS
- CI/CD pipelines: Jenkins, GitLab CI, ArgoCD
- Infrastructure as Code: Terraform, Pulumi
- Model deployment & serving: TensorFlow Serving, TorchServe, MLflow
- Data versioning & management: DVC, Pachyderm
- Monitoring & observability: Prometheus, Grafana, DataDog
- Programming: Python, Bash, Terraform HCL
- Version Control: Git, GitOps practices
Soft Skills
- Cross-team collaboration and communication
- Agile methodologies and DevSecOps mindset
- Problem-solving in high-pressure environments
- Continuous learning and technology evangelism
- Strong documentation and technical writing
WORK EXPERIENCE
*Senior MLOps Engineer*
*InnovateAI Solutions, New York, NY*
Jan 2023 – Present
- Led the migration of legacy ML pipelines to a fully serverless architecture on AWS, reducing deployment times by 35%.
- Designed and implemented automated CI/CD workflows integrating GitOps practices, enabling 24/7 model delivery with minimal manual intervention.
- Developed a scalable monitoring system with Prometheus and Grafana, decreasing model drift detection latency by 20%.
- Collaborated with data scientists to deploy models on Kubernetes clusters, ensuring high availability and fault tolerance.
*MLOps Engineer*
*DataStream Technologies, San Francisco, CA*
Aug 2020 – Dec 2022
- Built end-to-end CI/CD pipelines for AI products using Jenkins and ArgoCD, enabling rapid iteration cycles and consistent deployment across environments.
- Established an ML model registry utilizing MLflow, promoting reproducibility and streamlined experimentation.
- Implemented data versioning with DVC preventing data leakage issues and enhancing reproducibility.
- Automated infrastructure provisioning with Terraform, reducing setup time for new environments from days to hours.
*DevOps Engineer (Entry)*
*TechWave Inc., Remote*
Jul 2018 – Jul 2020
- Managed container orchestration for microservices system using Docker and Kubernetes, boosting deployment efficiency.
- Integrated security best practices into CI/CD workflows, ensuring compliance with enterprise standards.
- Developed monitoring dashboards with Grafana, improving incident response times.
EDUCATION
**Bachelor of Science in Computer Science**
University of California, Berkeley | Graduated May 2018
CERTIFICATIONS
- **Google Cloud Professional Machine Learning Engineer** (2024)
- **Certified Kubernetes Administrator (CKA)** (2023)
- **AWS Certified Solutions Architect – Associate** (2022)
PROJECTS
Model Deployment Automation Framework
- Developed a self-healing deployment pipeline using ArgoCD and Helm, reducing manual intervention for failure recovery by 40%.
Model Monitoring and Drift Detection System
- Implemented a comprehensive monitoring setup with DataDog and custom metrics, enabling proactive detection of model degradation across multiple services.
Multi-Cloud MLOps Platform
- Led a project deploying ML pipelines across AWS and Azure, achieving multi-region resilience and cost optimization, supporting rapid scale-up during peak demand.
TOOLS & TECHNOLOGIES
- AWS, Azure, GCP
- Kubernetes, Docker, Helm
- Jenkins, GitLab CI, ArgoCD
- Terraform, Pulumi
- MLflow, DVC, Pachyderm
- Prometheus, Grafana, DataDog
- TensorFlow Serving, TorchServe
LANGUAGES
- Python (Expert)
- Bash (Proficient)
- YAML, HCL (Intermediate)
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