Mlops Engineer In Cybersecurity Resume Example
Professional ATS-optimized resume template for Mlops Engineer In Cybersecurity positions
John Doe
MLOps Engineer – Cybersecurity
Email: johndoe@email.com | Phone: (123) 456-7890 | LinkedIn: linkedin.com/in/johndoe | GitHub: github.com/johndoe
PROFESSIONAL SUMMARY
Innovative MLOps Engineer specializing in deploying and scaling machine learning models within cybersecurity environments. Extensive experience integrating AI/ML solutions into security platforms, automating workflows, and ensuring robust model governance. Adept at collaboration across cross-functional teams to develop resilient, scalable, and secure ML pipelines leveraging cloud-native architectures and advanced monitoring. Passionate about leveraging AI for threat detection, vulnerability assessment, and anomaly detection in dynamic cyber landscapes.
SKILLS
Hard Skills
- MLOps Pipeline Development (Kubeflow, MLflow, Airflow)
- Containerization & Orchestration (Docker, Kubernetes)
- Cloud Platforms (AWS, Azure, GCP)
- CI/CD Automation (Jenkins, GitOps)
- Model Deployment & Monitoring (Prometheus, Grafana, DataDog)
- Cybersecurity ML Techniques (Anomaly Detection, Deep Learning for Threat Detection)
- Data Engineering (Spark, Kafka, ELK Stack)
- Programming (Python, Bash, Go)
- Version Control & Code Quality (Git, DataLad)
Soft Skills
- Cross-functional Collaboration
- Problem Solving in High-Pressure Environments
- Technical Documentation & Knowledge Sharing
- Agile & Scrum Methodologies
- Continuous Learning & Adaptation
- Cybersecurity Best Practices and Compliance
WORK EXPERIENCE
*Senior MLOps Engineer – Cybersecurity*
*CyberSecure Solutions, Remote*
June 2022 – Present
- Architected and maintained scalable ML pipelines for real-time threat detection, reducing false positives by 25%.
- Integrated ML models with SIEM systems, enabling automated anomaly classification from network logs and endpoint data.
- Led migration of deployment workflows to Kubernetes on AWS EKS, resulting in 40% reduction in model deployment time.
- Established model governance procedures, including versioning, drift detection, and audit trails in compliance with GDPR and ISO27001 standards.
- Collaborated with cybersecurity analysts to refine detection algorithms, incorporating zero-day threat signatures.
*Machine Learning Operations Engineer*
*SecureTech Labs, San Francisco, CA*
August 2019 – May 2022
- Streamlined model training, testing, and deployment workflows, reducing cycle time from weeks to days using CI/CD pipelines.
- Developed automated monitoring dashboards in Grafana for detecting model performance degradation during live operations.
- Optimized ML infrastructure with GPU-accelerated instances, improving model training speed by 35%.
- Implemented data pipelines with Kafka for ingesting streaming security logs, enabling proactive threat response.
- Conducted security assessments of AI models, mitigating vulnerabilities like model inversion and adversarial attacks.
Data Scientist (Entry-level)
*Innovo Cyber Defense, Boston, MA*
July 2017 – July 2019
- Designed anomaly detection algorithms for network traffic analysis, contributing to early breach detection initiatives.
- Assisted in data pipeline setup from raw log data to feature extraction, improving data readiness by 20%.
- Provided insights through visualizations that informed cybersecurity policy decisions.
EDUCATION
**Master of Science in Data Science**
Massachusetts Institute of Technology (MIT), Cambridge, MA
*2015 – 2017*
**Bachelor of Science in Computer Science**
University of California, Berkeley, CA
*2011 – 2015*
CERTIFICATIONS
- **AWS Certified Machine Learning – Specialty** (2023)
- **Certified Kubernetes Application Developer (CKAD)** (2022)
- **GCP Professional Machine Learning Engineer** (2023)
- **Cybersecurity & Data Privacy Certification** – (ISC)² (2021)
PROJECTS
ThreatHunter AI Platform
- Developed an AI-powered threat hunting platform utilizing unsupervised learning algorithms for detecting novel attack patterns.
- Deployed on GCP using Container-Optimized OS with Terraform, ensuring high availability and scalability.
- Enabled security analysts to proactively identify and investigate zero-day vulnerabilities.
Automated Incident Response Framework
- Built an automated pipeline that correlates alerts and generates incident reports, reducing response times by 60%.
- Integrated with SIEM and SOAR platforms, enhancing real-time defense capabilities in client networks.
TOOLS & TECHNOLOGIES
- **ML & Data Engineering:** TensorFlow, PyTorch, Spark, Kafka, Elasticsearch
- **MLOps Platforms:** Kubeflow, MLflow, DataRobot, Azure ML
- **Cloud Computing:** AWS (S3, Lambda, EKS), GCP (GKE, Vertex AI), Azure (ML Services)
- **Containerization & Orchestration:** Docker, Kubernetes, Helm
- **Monitoring & Logging:** Prometheus, Grafana, DataDog, ELK Stack
- **Version Control & CI/CD:** Git, Jenkins, GitOps, Argo CD
LANGUAGES
- Python (Expert)
- Bash (Advanced)
- Go (Intermediate)
- SQL (Proficient)
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