Mlops Engineer In Fintech Resume Example
Professional ATS-optimized resume template for Mlops Engineer In Fintech positions
Jane Doe
MLOps Engineer | Fintech Innovator
Email: janedoe@example.com | Phone: (123) 456-7890 | LinkedIn: linkedin.com/in/janedoe | GitHub: github.com/janedoe
PROFESSIONAL SUMMARY
Dynamic MLOps Engineer with over 5 years of experience in designing and scaling machine learning pipelines within the fintech sector. Proven expertise in deploying secure, compliant, and high-availability ML systems, automating model lifecycle management, and integrating advanced monitoring analytics. Passionate about leveraging automation, containerization, and cloud infrastructure to drive data-driven financial solutions. Adept at collaborating with cross-functional teams to deliver scalable models that enhance customer insights, credit scoring, and fraud detection.
SKILLS
Hard Skills
- MLOps & Model Lifecycle Management (MLflow, Kubeflow, TFX)
- Cloud Platforms (AWS, GCP, Azure)
- Containerization & Orchestration (Docker, Kubernetes)
- CI/CD Pipelines (Jenkins, GitHub Actions, GitLab CI)
- Data Engineering & ETL Processes
- Model Deployment & Monitoring (Prometheus, Grafana, Seldon)
- Security & Compliance (GDPR, PCI DSS)
- Programming (Python, Bash, SQL)
- Version Control & CI/CD Automation
- API Development & Microservices Architecture
Soft Skills
- Cross-functional Collaboration
- Analytical Problem Solving
- Agile Project Management
- Continuous Improvement & Adaptability
- Communication & Technical Documentation
- Stakeholder Engagement
WORK EXPERIENCE
*Senior MLOps Engineer*
*Fintech Solutions Inc., New York, NY*
June 2022 – Present
- Led the migration of ML models from development to production across multiple domains including risk assessment and fraud detection, reducing deployment time by 40%.
- Designed and implemented a scalable CI/CD pipeline utilizing GitHub Actions and Kubernetes, improving model update frequency while ensuring compliance.
- Developed real-time model performance dashboards leveraging Prometheus and Grafana, enabling proactive identification of drift and anomalies.
- Collaborated with data scientists to automate model retraining pipelines, decreasing manual intervention by 60%.
*MLOps Engineer*
*SecurePay Fintech, San Francisco, CA*
August 2019 – May 2022
- Built and maintained end-to-end ML pipelines for credit scoring, achieving 99.9% uptime and reducing latency by 30% via optimized deployment strategies.
- Managed containerized environments with Docker and Kubernetes, supporting rapid scaling during high-transaction periods.
- Integrated model versioning and lineage tracking through MLflow, facilitating auditability for compliance audits.
- Implemented security protocols for data encryption and access controls, aligning with GDPR and PCI DSS standards.
*Data Engineer & AI Developer*
*FinEdge Analytics, Remote*
July 2017 – July 2019
- Developed ETL workflows to prepare large datasets for machine learning models, streamlining data ingestion from multiple financial data sources.
- Initiated migration of analytics workloads to GCP’s AI Platform, enhancing collaborative model training capabilities.
- Collaborated with ML teams to automate feature extraction processes, resulting in faster model iteration cycles.
EDUCATION
**Bachelor of Science in Computer Science**
University of California, Berkeley
*September 2013 – June 2017*
CERTIFICATIONS
- Google Cloud Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
- Certified Kubernetes Administrator (CKA)
- DataRobot Automated Machine Learning Certification
PROJECTS
Automated Fraud Detection System
- Designed a scalable MLOps framework integrating TensorFlow Extended (TFX) on GCP, enabling continuous training and deployment of fraud detection models with real-time monitoring.
Credit Risk Modeling Pipeline
- Developed a microservices architecture for credit scoring, utilizing Docker, Kubernetes, and CI/CD pipelines to facilitate agile model updates while maintaining compliance standards.
TOOLS & TECHNOLOGIES
- **ML Frameworks:** TensorFlow, PyTorch, Scikit-learn
- **Platforms:** GCP, AWS, Azure
- **Orchestration:** Kubernetes, Docker Swarm
- **Pipeline Tools:** MLflow, Kubeflow, TFX, Airflow
- **Monitoring:** Prometheus, Grafana, Seldon
- **Coding:** Python, Bash, SQL
LANGUAGES
- English (Native)
- Spanish (Professional Working Proficiency)
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