Machine Learning Operations Engineer

Overview

Join us as we Rise to the Challenge

 

At KCI, we’re building an enduring community that provides unparalleled value to our employee-owners. We make our mark designing and delivering our world-class solutions, so we invest deeply in supporting and developing our team. We reward integrity and commitment, and when we do well, you do well. Our employee’s have the freedom to innovate, unlimited growth, a voice that matters, a lifestyle that works, and skin in the game. Achievements are shared and celebrated. As a team, we are motivated to better ourselves, each other, and the world around us. 

 

THE COMPANY

KCI Technologies, Inc. is a 100% employee-owned engineering, consulting and construction firm serving clients throughout the United States. KCI is recognized as an industry leader, employing cutting-edge technologies, management practices and strategic growth initiatives. Employee ownership fosters an entrepreneurial spirit, encourages technical expertise, and shapes strategic planning.

KCI is currently ranked #56 on Engineering News-Record’s list of the Top 500 design firms in the nation.

 

KCI BENEFITS INFORMATION

We offer a competitive compensation package, family friendly benefits, a collaborative working environment, and the training, mentoring and resources you need to advance in your career.

 

We understand that you have choices, and we know that together we will make a great team!

 

KCI is committed to building a diverse and inclusive staff, and we encourage women, people of color, LGBTQ+ individuals, and individuals with disabilities to apply.

 

KCI Technologies, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.

Duties, Responsibilities & Other

Key Responsibilities:

• Model Selection: Leverage analytical skills and prototyping ability to select proper model architectures and algorithms, using frameworks, libraries, and foundational models to meet the requirements determined by the product team.
• Model Training: Curate engineering data sources including wild data being processed by existing models to support multiple models across multiple engineering domains, using the data to build learning data sets to create models and improve them over time.
• Model Analysis: Drive metrics-based model analysis to grade performance and to manage the elevation and re-training of models.
• Model Deployment: Design and implement automated deployment pipelines for machine learning models, ensuring seamless integration from development to production environments.
• Infrastructure Management: Develop and maintain the infrastructure required for continuous integration, continuous delivery (CI/CD), and continuous training (CT) of machine learning models.
• Monitoring and Maintenance: Implement monitoring and logging solutions to track model performance and system health, ensuring high availability and quick incident response.
• Collaboration: Work closely with data scientists to understand model requirements and ensure that the infrastructure supports the experimental and production needs.
• Optimization: Identify and implement strategies to optimize model performance and reduce latency in real-time serving environments.
• Security and Compliance: Ensure that all machine learning pipelines comply with industry standards and best practices for security and data privacy.
• Documentation: Create and maintain comprehensive documentation for all processes, infrastructure configurations, and deployment strategies.

Specialized experience:

• Experience
• Hands-on experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, RoboFlow, scikit-learn, etc.
• Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Proficiency in CI/CD tools like Pipelines, Jenkins, GitLab CI.
•  Technical Skills
• Strong programming skills in languages such as Python, R, Java, Scala, or C++.
• Familiarity with infrastructure-as-code tools like Terraform, Bicep, and CloudFormation.
• Experience with version control systems, particularly Git.
• Knowledge of monitoring and logging tools like Application Insights, Log Analytics, Cloud Watch/Trail, a