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

*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.

**Bachelor of Science in Computer Science**

University of California, Berkeley | Graduated May 2018

- **Google Cloud Professional Machine Learning Engineer** (2024)

- **Certified Kubernetes Administrator (CKA)** (2023)

- **AWS Certified Solutions Architect – Associate** (2022)

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.

- AWS, Azure, GCP

- Kubernetes, Docker, Helm

- Jenkins, GitLab CI, ArgoCD

- Terraform, Pulumi

- MLflow, DVC, Pachyderm

- Prometheus, Grafana, DataDog

- TensorFlow Serving, TorchServe

- Python (Expert)

- Bash (Proficient)

- YAML, HCL (Intermediate)