Machine Learning Operations Engineer

$85,000 - $130,000/Yr

KCI Technologies - Sparks, MD

Full-time - Mid Level

Sparks, MD

51-100 employees

Professional, Scientific, and Technical Services

About the position

The Machine Learning Operations Engineer at KCI Technologies, Inc. is responsible for designing, implementing, and maintaining machine learning models and their deployment pipelines. This role involves collaborating with data scientists, managing infrastructure for continuous integration and delivery, and ensuring compliance with industry standards. The engineer will leverage analytical skills to select appropriate model architectures, curate data sources, and optimize model performance, contributing to the overall success of machine learning initiatives within the company.

Responsibilities

• Leverage analytical skills to select proper model architectures and algorithms.
• Curate engineering data sources to support multiple models across various engineering domains.
• Drive metrics-based model analysis to grade performance and manage model re-training.
• Design and implement automated deployment pipelines for machine learning models.
• Develop and maintain infrastructure for continuous integration, delivery, and training of models.
• Implement monitoring and logging solutions to track model performance and system health.
• Collaborate with data scientists to understand model requirements and infrastructure needs.
• Identify and implement strategies to optimize model performance and reduce latency.
• Ensure compliance of machine learning pipelines with industry standards for security and data privacy.
• Create and maintain comprehensive documentation for processes and deployment strategies.

Requirements

• Bachelor's degree in Computer Science, Math, Data Science, or a related field.
• Minimum 5 years of experience in software engineering, DevOps, data analytics, data science, or a similar role.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Proficiency in CI/CD tools like Jenkins and GitLab CI.
• Strong programming skills in Python, R, Java, Scala, or C++.
• Familiarity with infrastructure-as-code tools like Terraform and CloudFormation.
• Experience with version control systems, particularly Git.
• Knowledge of monitoring and logging tools like Application Insights and Splunk.

Nice-to-haves

• Master's degree in Computer Science, Math, Data Science, or a related field.
• Experience working in an MLOps environment such as MLflow or Neptune.
• Proven machine learning experience using tools such as Databricks ML or Azure ML.

Benefits

• Competitive compensation package
• Family-friendly benefits
• Collaborative working environment
• Training, mentoring, and resources for career advancement