Mlops Engineer Resume Example
Professional ATS-optimized resume template for Mlops Engineer positions
John Doe
Lead MLOps Engineer
Email: example@email.com | Phone: (123) 456-7890
PROFESSIONAL SUMMARY
Innovative MLOps Engineer with over 5 years of experience designing, deploying, and maintaining scalable machine learning pipelines in cloud environments. Expertise in CI/CD automation for ML models, model versioning, and performance monitoring across dynamic production systems. Passionate about bridging the gap between data science and operations to deliver robust AI solutions that drive business value. Adept at implementing best practices for reproducibility, security, and cost efficiency in fast-paced, cloud-native ecosystems.
WORK EXPERIENCE
**InnovateAI Solutions**, San Francisco, CA
June 2022 – Present
- Architected and implemented end-to-end ML pipelines on AWS, reducing model deployment time from weeks to hours.
- Established automated CI/CD workflows with Jenkins and GitHub Actions, improving deployment reliability and rollback capabilities.
- Integrated Prometheus and Grafana for real-time model performance monitoring, enabling proactive alerting and troubleshooting.
- Developed custom Terraform modules for infrastructure provisioning, enhancing consistency across environments.
- Collaborated with data scientists to maintain version control and reproducibility for over 50 ML models in production.
*MLOps Engineer*
**DataX Technologies**, New York, NY
March 2019 – May 2022
- Managed deployment of ML models on GCP, leveraging Vertex AI and Kubernetes Engine, achieving 99.9% uptime.
- Designed containerized ML workflows with Docker, orchestrated with Helm charts, simplifying scaling and updates.
- Implemented model governance frameworks, including automated validation and audit trails aligned with GDPR standards.
- Led initiatives to reduce cloud infrastructure costs by 20% via resource optimization and spot instances.
- Automated data ingestion and preprocessing pipelines to support real-time analytics, integrating Apache Kafka and Airflow.
*Data Engineer / DevOps Intern*
**FinTech Innovations**, Boston, MA
Summer 2018
- Assisted in building ETL pipelines for credit scoring algorithms; improved data throughput by 30%.
- Supported migration of legacy systems to cloud infrastructure, gaining hands-on experience with AWS and Docker.
EDUCATION
**Bachelor of Science in Computer Science**
University of California, Berkeley — 2015–2019
CERTIFICATIONS
- **Google Cloud Professional Machine Learning Engineer** (2021)
- **AWS Certified Machine Learning – Specialty** (2022)
- **Kubernetes Certified Administrator (CKA)** (2023)
PROJECTS
Model Monitoring Dashboard
Developed a comprehensive real-time dashboard using Prometheus and Grafana that visualizes model performance metrics, drift detection, and resource utilization, reducing troubleshooting time by 40%.
Automated ML Pipeline Framework
Built a reusable Python-based framework integrating MLflow and Kubernetes for continuous deployment of models, leading to a 3x improvement in deployment speed across multiple projects.
Cost Optimization Automation Script
Created Terraform scripts and shell automation to dynamically provision cloud resources based on workload, leading to an annual saving of approximately $50,000.
TOOLS & TECHNOLOGIES
- **Languages:** Python, Bash, YAML<br>
- **Cloud:** AWS (S3, EC2, EKS), GCP (Vertex AI, Cloud Run), Azure<br>
- **Containers & Orchestration:** Docker, Kubernetes, Helm<br>
- **CI/CD:** Jenkins, GitHub Actions, GitLab CI<br>
- **Monitoring & Visualization:** Prometheus, Grafana, ELK Stack<br>
- **Data & Workflow Management:** Apache Airflow, Kafka, Spark<br>
- **ML Frameworks:** TensorFlow, PyTorch, scikit-learn
- **Infrastructure as Code:** Terraform, CloudFormation
LANGUAGES
- English (Native)
- Spanish (Professional working proficiency)
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