Mlops Engineer In Healthcare Resume Example
Professional ATS-optimized resume template for Mlops Engineer In Healthcare positions
John A. Doe
MLOps Engineer | Healthcare AI Specialist
Email: johndoe@email.com | Phone: (555) 123-4567 | LinkedIn: linkedin.com/in/johndoe | Location: Boston, MA
PROFESSIONAL SUMMARY
Innovative MLOps Engineer with over 5 years of experience driving scalable machine learning infrastructure in healthcare environments. Adept at deploying HIPAA-compliant models, orchestrating real-time data pipelines, and ensuring robustness and compliance of AI systems in clinical settings. Skilled in cloud-native solutions, containerization, and automated monitoring, with a proven track record of accelerating AI deployment cycles and enhancing model reliability. Passionate about leveraging emerging technologies to improve patient outcomes and streamline healthcare operations.
SKILLS
Hard Skills
- Cloud Platforms: AWS, Azure, GCP
- CI/CD Pipelines: Jenkins, GitLab CI, Argo CD
- Containerization & Orchestration: Docker, Kubernetes, OpenShift
- Machine Learning Pipelines: MLflow, Kubeflow, TFX
- Data Engineering: Apache Spark, Kafka, Airflow
- Healthcare Data Standards: HL7, FHIR, DICOM, HIPAA compliance
- Monitoring & Logging: Prometheus, Grafana, ELK Stack
- Programming Languages: Python, Bash, Go
Soft Skills
- Cross-Functional Collaboration
- Agile Methodologies & DevSecOps
- Problem Solving & Critical Thinking
- Effective Communication for Technical & Non-Technical Stakeholders
- Adaptability in Dynamic Healthcare Environments
WORK EXPERIENCE
*Senior MLOps Engineer*
*InnovateHealth AI Solutions | Boston, MA*
June 2022 – Present
- Led the development of a HIPAA-compliant MLOps platform that streamlined deployment of clinical risk prediction models, reducing time-to-market by 35%.
- Designed automated data pipelines using Apache Airflow and Kafka, enabling real-time analytics on patient vital signs and lab results with less than 5 minutes latency.
- Implemented robust monitoring with Prometheus and Grafana, reducing model drift and ensuring the continuous reliability of deployed healthcare models.
- Collaborated with data scientists to containerize models using Docker and orchestrate deployments via Kubernetes, facilitating seamless rollbacks and updates.
*MLOps Engineer*
*MedTech Solutions | Cambridge, MA*
August 2019 – May 2022
- Developed scalable ML workflows leveraging TFX and Kubeflow Pipelines, reducing manual intervention and deployment errors for diagnostic image analysis models.
- Managed cloud infrastructure on AWS, optimizing compute and storage costs through auto-scaling, spot instances, and resource tagging.
- Ensured compliance with healthcare regulations by implementing security best practices, Role-Based Access Control (RBAC), and data encryption.
- Conducted regular audits and automated compliance checks, resulting in passing all HIPAA audits with zero compliance issues.
*Data Engineer (Healthcare Focus)*
*HealthData Inc. | New York, NY*
July 2017 – July 2019
- Built ETL processes for ingesting and transforming HL7 and FHIR data streams, supporting a machine learning platform for predictive analytics.
- Enabled integration of DICOM imaging metadata into centralized data lakes, facilitating clinical research projects.
- Collaborated with clinical staff to optimize data pipelines for accuracy and efficiency, improving reporting speed by 20%.
EDUCATION
**Bachelor of Science in Computer Science**
Massachusetts Institute of Technology (MIT) | 2013 – 2017
CERTIFICATIONS
- AWS Certified Machine Learning – Specialty
- Certified Kubernetes Administrator (CKA)
- HIPAA Data Privacy & Security Certification (HITRUST CSF)
- Google Cloud Professional Data Engineer
PROJECTS
Real-Time Clinical Alert System
- Developed an end-to-end pipeline deploying deep learning models for early sepsis detection. Used live patient data streams from hospital systems and integrated with hospital alerting infrastructure to notify clinicians within seconds of risk detection. Achieved 85% sensitivity and 90% specificity in pilot deployments.
Automated Model Compliance Auditor
- Created an automated framework that scans deployed models against regulatory and internal standards, highlighting potential compliance issues before deployment. Reduced compliance review time from days to hours.
TOOLS & TECHNOLOGIES
- Cloud: AWS, Azure, GCP
- CI/CD: Jenkins, GitLab CI, Argo CD
- Orchestration: Kubernetes, OpenShift
- Data & ML Pipelines: MLflow, Kubeflow, TFX
- Monitoring: Prometheus, Grafana, ELK Stack
- Programming: Python, Bash, Go
- Healthcare Standards: HL7, FHIR, DICOM
LANGUAGES
- English (Native)
- Spanish (Professional Working Proficiency)
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