Mlops Engineer In Ai Resume Example
Professional ATS-optimized resume template for Mlops Engineer In Ai positions
John Doe
Hard Skills:
Email: example@email.com | Phone: (123) 456-7890
PROFESSIONAL SUMMARY
Innovative MLOps Engineer with over 5 years of experience in designing, deploying, and maintaining scalable machine learning pipelines in cloud environments. Adept at integrating ML models into production workflows, optimizing model performance, and ensuring operational efficiency with a focus on automation and security. Skilled in cloud-native architectures, CI/CD pipelines, and monitoring ML systems at scale. Passionate about advancing AI deployment methodologies to enable rapid, reliable, and responsible AI solutions.
SKILLS
- Cloud Platforms: AWS (S3, SageMaker, Lambda, EKS), Azure ML, GCP (Vertex AI)
- Containerization & Orchestration: Docker, Kubernetes, Helm
- CI/CD Pipelines: Jenkins, GitLab CI, CodePipeline
- ML Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost
- Data & Model Management: MLflow, DVC, Neptune.ai
- Infrastructure as Code: Terraform, CloudFormation
- Monitoring & Logging: Prometheus, Grafana, ELK Stack, Seldon Core
- Scripting & Automation: Python, Bash, YAML
**Soft Skills:**
- Cross-functional Collaboration
- Agile Methodologies
- Problem Solving & Critical Thinking
- Communication & Documentation
- Adaptability to Rapid Tech Changes
WORK EXPERIENCE
*Senior MLOps Engineer*
*InnovateAI Solutions, San Francisco, CA*
June 2022 – Present
- Led the redesign of the ML deployment architecture, reducing model deployment time by 40%, enabling near real-time AI inference for customer-facing applications.
- Developed scalable CI/CD pipelines using GitLab CI and Kubernetes, integrating automated testing, security scans, and model validation.
- Implemented a robust monitoring system with Prometheus and Grafana, achieving proactive detection of data drift and model performance issues, reducing downtime by 25%.
- Collaborated with Data Scientists to containerize models with Docker, optimize resource utilization, and deploy via AWS SageMaker and EKS.
- Standardized model versioning and reproducibility workflows with MLflow and DVC, improving collaboration and auditability across teams.
*MLOps Engineer*
*NextGen AI Labs, New York, NY*
August 2019 – May 2022
- Built end-to-end ML pipelines for predictive analytics using GCP Vertex AI, increasing deployment efficiency by 35%.
- Automated data ingestion and cleaning processes with Apache Beam and Cloud Dataflow, ensuring high data quality for training datasets.
- Managed infrastructure using Terraform, deploying scalable TensorFlow Serving environments on Kubernetes clusters.
- Developed custom monitoring dashboards for model drift and resource utilization, improving system reliability.
- Worked closely with Data Engineers and Data Scientists to streamline training workflows and enable continuous experimentation.
*Data Engineer / DevOps Associate*
*TechStart Inc., Boston, MA*
July 2017 – July 2019
- Supported data pipeline development and cloud infrastructure automation to facilitate ML model deployment projects.
- Implemented CI/CD practices using Jenkins and Docker containers, reducing deployment errors and lead time.
- Contributed to setting up logging and alerting systems, improving incident response times.
EDUCATION
**Bachelor of Science in Computer Science**
Massachusetts Institute of Technology (MIT)
Graduated: May 2017
CERTIFICATIONS
- **AWS Certified Machine Learning - Specialty** (2023)
- **Google Cloud Professional Machine Learning Engineer** (2022)
- **Kubernetes Administrator (CKA)** (2021)
PROJECTS
- **Automated ML Model Deployment Platform:** Developed a unified platform leveraging Kubernetes, Helm, and CI/CD pipelines, allowing Data Scientists to deploy models with a single command, reducing deployment cycle from days to hours.
- **Model Monitoring & Drift Detection System:** Created an anomaly detection system integrated with Prometheus, Grafana, and custom alerting, which predicted model performance degradation resulting in proactive retraining strategies.
- **AI Resource Optimization Pipeline:** Implemented resource autoscaling and GPU management with TensorFlow and Kubernetes, leading to a 50% reduction in cloud costs for large-scale model training.
LANGUAGES
- **Python** (Expert)
- **Bash** (Intermediate)
- **YAML** (Proficient)
Build Resume for Free
Create your own ATS-optimized resume using our AI-powered builder. Get 3x more interviews with professionally designed templates.