Vaco is searching for a seasoned Senior MLOps Engineer to help bridge the gap between model development and real-world impact. In this role, you'll be the driving force behind the infrastructure, tools, and systems that turn machine learning concepts into reliable, real-time predictions in production. You'll work closely with cross-functional teams-Data Science, Product Engineering, Data Product Management, and Data Platform-to streamline and automate how models are deployed and maintained.
What You'll Do - Build and maintain scalable, production-grade ML infrastructure that supports everything from training to deployment and monitoring.
- Enhance orchestration processes to enable seamless model deployment and lifecycle management.
- Continuously optimize system performance to deliver high-speed, cost-efficient solutions.
- Ensure the ML environment is secure, scalable, and always running at peak performance.
- Collaborate with data scientists and software engineers to build robust pipelines and infrastructure for end-to-end ML workflows.
- Create tools for data analysis, experiment tracking, model versioning, and artifact management-while also supporting compliance and governance needs.
- Develop monitoring systems that can detect drift, track model health, and ensure long-term stability in production.
- Automate repetitive processes using custom tools and scripts to improve MLOps speed and reliability.
- Guide and mentor team members, sharing technical insights and helping resolve complex issues.
- Act as a champion for data integrity, security, and risk management throughout the ML lifecycle.
- Adapt to evolving priorities and pitch in on other tasks as needed by your team or leadership.
Minimum Requirements - Bachelor's degree in Computer Science, Engineering, or related field.
- At least 5 years of experience in Data Science, Machine Learning Engineering, or MLOps.
- Advanced Python skills and familiarity with ML libraries and frameworks.
- Hands-on experience with AWS and cloud-based architecture.
- Solid knowledge of container tools like Docker and Kubernetes, and CI/CD best practices.
- Experience deploying ML models using tools such as MLflow, Kubeflow, or similar platforms.
- Strong foundation in data infrastructure, distributed systems, and scalable software architecture.
- Demonstrated ability to deliver models that perform reliably in real-time production systems.
- Ability to pass background and drug screenings.
Preferred Experience - Experience working in hybrid environments (cloud/on-premise).
- Familiarity with big data ecosystems like Hadoop, Hive, or Cloudera.
- Skills in distributed computing using Spark or similar frameworks.
- Proficiency with Java or Scala in data engineering or ML contexts.
Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual's skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company's 401(k) retirement plan.