Professional Summary
Results-driven DevOps and MLOps Engineer with a strong background in building, automating, and optimizing secure and scalable infrastructure for enterprise systems. Skilled in CI/CD automation, containerization, orchestration, cloud computing (AWS, Azure), and infrastructure as code (Terraform, Ansible). Expert collaborator with data science teams to streamline model training, deployment, and monitoring using tools such as MLflow, SageMaker, and Airflow. Proven ability to reduce release cycles, enhance deployment reliability, and support full lifecycle management of machine learning models.
Professional Experience
IT Concepts – Baltimore, MD
DevOps & MLOps Engineer | 05/2019 – Present
DevOps Initiatives
- Designed and managed cloud-native infrastructure using Terraform and AWS CloudFormation, improving provisioning speed by 60%.
- Automated deployment pipelines using GitLab CI/CD and Jenkins, enabling faster and more reliable release cycles.
- Implemented secure and scalable container deployments using Docker and Kubernetes (EKS), supporting blue/green and canary rollouts.
- Centralized logging and monitoring using ELK Stack, Prometheus, and Grafana for real-time system observability.
MLOps Implementation
- Built end-to-end ML pipelines using MLflow for model tracking, versioning, and deployment.
- Integrated Airflow for orchestrating batch feature extraction, model training, and evaluation workflows.
- Deployed models on AWS SageMaker, configuring endpoints with autoscaling and lifecycle hooks for drift handling.
- Established model monitoring dashboards with Prometheus and alerting via CloudWatch for uptime and performance metrics.
Collaboration & Security
- Worked cross-functionally with Data Scientists, Security Engineers, and Platform Teams to streamline production readiness for ML models.
- Implemented secure secrets and IAM policies for fine-grained access control using AWS IAM and Secrets Manager.
- Used Postman and custom Python scripts to test RESTful inference APIs in staging and production.
Technologies: Docker, Kubernetes, Terraform, Jenkins, GitLab CI, MLflow, Airflow, SageMaker, Prometheus, Grafana, ELK Stack, Python, AWS
Leidos – Baltimore, MD
DevOps Engineer | 06/2017 – 05/2019
- Designed and implemented CI/CD pipelines using Jenkins and GitHub Actions to automate infrastructure deployments and configuration updates.
- Led containerization of legacy applications and internal tools using Docker, and managed orchestration using Kubernetes (self-hosted clusters).
- Developed monitoring solutions with Elasticsearch, Logstash, and Kibana (ELK) to improve visibility across distributed systems.
- Automated infrastructure and configuration changes using Terraform and Ansible, reducing manual intervention by 80%.
- Created secure access workflows using AWS IAM, enforced least-privilege principles, and integrated with VPN/SSO authentication layers.
Technologies: Docker, Jenkins, Ansible, Terraform, Kubernetes, ELK, AWS, GitHub Actions, JIRA, Bitbucket
Lockheed Martin – Baltimore, MD
DevOps Engineer | 12/2015 – 06/2017
- Designed containerized development and test environments using Docker Compose and Kubernetes, accelerating local onboarding and QA cycles.
- Implemented infrastructure monitoring and alerts with Prometheus and Grafana, enabling proactive troubleshooting and reduced downtime.
- Built and managed Jenkins pipelines for testing, linting, vulnerability scanning, and packaging infrastructure modules.
- Automated deployments of microservices and configuration changes across staging and production using GitOps principles.
- Deployed and managed internal cloud infrastructure using AWS, including S3 for artifacts, Lambda for automation, and CloudWatch for logging.
Technologies: Jenkins, Docker, Kubernetes, Terraform, Prometheus, Grafana, Git, AWS, Bash, Python