Machine Learning Operations Engineer

Vintti bridges the staffing gap for US businesses with a unique focus on time zone compatibility. We source top talent from Latin America, offering companies access to professionals who are available during standard US working hours. This alignment eliminates the need for off-hour communications and allows for integrated teamwork, as if all team members were in the same office.

A Machine Learning Operations (MLOps) Engineer is responsible for integrating machine learning models into production environments seamlessly and efficiently. This role bridges the gap between data science and IT operations, ensuring that ML models are not only deployed successfully but also monitored, maintained, and scaled effectively. MLOps Engineers work on automating workflows, managing ML infrastructure, and optimizing performance to support continuous development and deployment. They play a critical part in improving the reliability, scalability, and overall lifecycle management of machine learning solutions within an organization.

- Bachelor's degree in Computer Science, Engineering, or a related field
- 3+ years of experience in machine learning operations or a similar role
- Proficiency with cloud platforms such as AWS, GCP, or Azure
- Strong experience with container orchestration tools like Kubernetes
- Expertise in implementing CI/CD pipelines for machine learning projects
- Proficient in programming languages such as Python, Java, or C++
- Hands-on experience with ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn
- Deep understanding of data pipelines and ETL processes
- Experience with monitoring and alerting tools like Prometheus and Grafana
- Familiarity with automated testing frameworks and deployment processes for ML models
- Solid knowledge of GPU management and optimization for ML workloads
- Strong troubleshooting and problem-solving skills in production environments
- Excellent collaboration and communication skills
- Knowledge of security best practices and privacy regulations related to ML systems
- Proficiency in version control tools such as Git
- Experience with performance tuning and scaling of APIs for ML models
- Familiarity with data versioning and experiment tracking tools like MLflow or DVC
- Ability to document processes, system configurations, and code
- Strong analytical and organizational skills
- Demonstrated ability to stay up-to-date with the latest trends, tools, and technologies in ML operations

- Monitor and maintain machine learning models in production
- Develop, deploy, and manage scalable ML infrastructure using cloud platforms and container orchestration tools
- Implement CI/CD pipelines for ML projects
- Troubleshoot production environment issues
- Optimize system performance and identify bottlenecks
- Collaborate with data scientists and software engineers
- Conduct regular retraining and evaluation of ML models
- Ensure reliable, efficient, and scalable data pipelines
- Implement monitoring and alerting frameworks
- Maintain and improve automated testing frameworks and deployment processes
- Manage and optimize hardware resources for ML workloads
- Document processes, system configurations, and code
- Stay current with trends, tools, and technologies in ML operations
- Coordinate with security teams for compliance with privacy regulations
- Conduct performance tuning and scaling of APIs for ML models
- Implement data versioning and experiment tracking tools

The ideal candidate for the Machine Learning Operations Engineer role is a highly skilled and motivated professional with a bachelor's degree in Computer Science, Engineering, or a related field, and over three years of experience in machine learning operations or a similar capacity. They possess a deep proficiency with major cloud platforms such as AWS, GCP, or Azure, and have extensive experience with container orchestration tools like Kubernetes. Their technical acumen includes expertise in implementing CI/CD pipelines, programming fluently in languages such as Python, Java, or C++, and hands-on experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. They have a robust understanding of data pipelines and ETL processes, as well as experience with monitoring and alerting tools such as Prometheus and Grafana. Strong familiarity with automated testing frameworks, deployment processes, GPU management, and performance tuning of APIs is essential. The ideal candidate excels in troubleshooting, problem-solving, documenting processes, and collaborating effectively with both data scientists and software engineers. Their knowledge of security best practices, version control tools like Git, and data versioning tools like MLflow or DVC is comprehensive. Personal attributes include being highly proactive, detail-oriented, adaptable, and possessing excellent communication and organizational skills, along with the capability to mentor junior team members, handle multiple tasks, and continuously stay abreast of evolving ML trends and technologies. This candidate is results-driven, customer-focused, innovative, and exhibits strong analytical and critical thinking skills, making them an indispensable team player in a fast-paced, dynamic work environment.

- Monitor and maintain machine learning models in production to ensure high availability and performance
- Develop, deploy, and manage scalable machine learning infrastructure using cloud platforms and container orchestration tools like Kubernetes
- Implement continuous integration/continuous deployment (CI/CD) pipelines for machine learning projects to automate the training and deployment of models
- Troubleshoot issues in production environments, identifying bottlenecks and optimizing system performance
- Collaborate with data scientists and software engineers to streamline the model development lifecycle
- Conduct regular retraining and evaluation of machine learning models to maintain accuracy and relevance
- Ensure data pipelines are reliable, efficient, and scalable, collecting and processing large datasets for model training and inference
- Implement monitoring and alerting frameworks using tools like Prometheus and Grafana to track the health and performance of machine learning systems
- Maintain and improve automated testing frameworks and deployment processes for machine learning models
- Manage and optimize the utilization of hardware resources, including GPUs and storage, for machine learning workloads
- Document processes, system configurations, and code to facilitate knowledge sharing and onboarding of new team members
- Stay up-to-date with the latest trends, tools, and technologies in machine learning operations and suggest improvements to existing workflows
- Coordinate with security teams to ensure that machine learning systems comply with privacy regulations and security protocols
- Conduct performance tuning and scaling of APIs that serve machine learning models to meet user demand
- Implement data versioning and experiment tracking tools to ensure reproducibility of machine learning experiments

- Proactive and self-motivated
- Detail-oriented with a focus on quality
- Strong problem-solving abilities
- Excellent teamwork and collaboration skills
- Ability to work in a fast-paced environment
- Strong communication skills
- Intrinsic curiosity and willingness to learn
- Adaptability to evolving technologies and workflows
- Strong analytical and critical thinking skills
- Results-driven and goal-oriented
- High level of responsibility and ownership
- Strong organizational skills
- Customer-focused mindset
- Ability to handle and prioritize multiple tasks
- Innovativeness and creativity in solution development
- Strong technical acumen
- Ability to mentor and guide junior team members

- Competitive salary, ranging from $90,000 to $150,000 based on experience and expertise
- Comprehensive health insurance plans, including medical, dental, and vision coverage
- Flexible working hours and remote work options
- Generous paid time off (PTO) policy, including vacation, holidays, and personal days
- Retirement savings plan with employer matching contributions
- Professional development opportunities, including training programs, workshops, and conferences
- Stock options or equity participation plans
- Wellness programs and resources, including gym memberships or fitness reimbursement
- Employee assistance programs (EAP) for mental health and wellbeing
- Paid parental leave and family support benefits
- Tuition reimbursement for further education and certifications
- Modern and ergonomic office environment with the latest technology and tools
- Employee recognition and reward programs
- Opportunities for career growth and advancement
- Collaborative and inclusive company culture
- Regular team-building activities and company events
- Company-provided hardware and software for remote work
- Commuter benefits or transportation allowance
- Employee discounts on company products and services