DevOps / MLOps Engineer - Assistant Vice President

Deutsche Bank • Cary, NC

4d • $100,000 - $142,250 • Hybrid

About The Position

We are seeking a skilled DevOps/MLOps Engineer with deep expertise in Google Cloud Platform (GCP) to help us build a world-class machine learning and AI capabilities within the bank. You will be instrumental in designing, implementing, and maintaining scalable infrastructure and automated pipelines that support the full machine learning lifecycle—from experimentation to deployment and monitoring. This role involves close collaboration with data scientists, data engineers, product managers, and platform teams to operationalize models, streamline workflows, and uphold the highest standards of security, privacy, and compliance. You’ll help define and evolve our MLOps practices, ensuring our AI solutions are reliable, reproducible, and impactful.

Requirements

• Bachelor's degree or equivalent is required
• Experience in MLOps or DevOps roles, with a strong focus on cloud-native ML infrastructure
• Proven experience deploying ML models in production (batch and real-time), ideally in regulated or privacy-sensitive environments.
• Proficiency in Python, with solid software engineering fundamentals and experience using Terraform or Deployment Manager for infrastructure-as-code
• Hands-on experience with GCP ML tools: Vertex AI, AI Platform, BigQuery ML and CI/CD: Cloud Build, GitHub Actions, Jenkins
• Containerization & Orchestration: Docker, Kubernetes, GKE

Nice To Haves

• Experience deploying and managing generative AI models (LLMs) in production, including prompt engineering, evaluation pipelines, and safety guardrails
• Familiarity with observability tools such as MLflow, LangFuse, or Braintrust
• Exposure to data governance and privacy frameworks in cloud environments

Responsibilities

• Build and maintain CI/CD pipelines for ML workflows using GCP-native tools such as Cloud Build, Artifact Registry, and Cloud Deploy
• Containerize and orchestrate ML workloads using Docker, Kubernetes, and GKE (Google Kubernetes Engine)
• Collaborate with cross-functional teams to transition models from development to production, integrating them into customer-facing applications.
• Implement robust model monitoring, logging, and alerting using tools like Vertex AI Model Monitoring, Cloud Logging, and Cloud Monitoring
• Define and enforce best practices for model versioning, testing, and reproducibility using tools like MLflow and Vertex AI Pipelines
• Ensure infrastructure adheres to s ecurity and compliance standards, working closely with Cybersecurity and Data Governance teams

Benefits

• A diverse and inclusive environment that embraces change, innovation, and collaboration
• A hybrid working model with up to 60% work from home, allowing for in-office / work from home flexibility, generous vacation, personal and volunteer days
• A commitment to Corporate Social Responsibility
• Employee Resource Groups support an inclusive workplace for everyone and promote community engagement
• Access to a strong network of Communities of Practice connecting you to colleagues with shared interests and values
• Competitive compensation packages including health and wellbeing benefits, retirement savings plans, parental leave, and family building benefits
• Educational resources, matching gift, and volunteer programs

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees