Clinical Data ML Ops Engineer

Cognizant Technology Solutions • Chicago, IL

71d • $115,000 - $135,000

About The Position

The Clinical Data ML Ops Engineer will be responsible for developing and programming integrated software algorithms to structure, analyze, and leverage data in product and systems applications in both structured and unstructured environments. The role requires expertise in LLM models and prompt engineering, as well as the ability to develop and communicate descriptive, diagnostic, predictive, and prescriptive insights and algorithms. The engineer will utilize machine learning and statistical modeling techniques to improve product/system performance, quality, data management, and accuracy. This position also involves translating algorithms and technical specifications into code, performing testing and debugging, and documenting procedures for installation and maintenance. Additionally, the role includes applying deep learning technologies and adapting machine learning to various interactive products. MLOps responsibilities include designing and implementing cloud solutions, building MLOps pipelines, and collaborating with data scientists and engineers.

Requirements

• Ability to design and implement cloud solutions and build MLOps pipelines on AWS, Azure, or GCP.
• Experience with MLOps frameworks like Kubeflow, MLFlow, DataRobot, and Airflow.
• Proficiency in Docker, Kubernetes, and OpenShift.
• Knowledge of programming languages such as Python, Go, Ruby, or Bash.
• Good understanding of Linux and frameworks like scikit-learn, Keras, PyTorch, and TensorFlow.
• Experience with software development and test automation.
• Fluent in English with good communication skills and ability to work in a team.

Responsibilities

• Develop and program integrated software algorithms for data analysis in product and systems applications.
• Utilize LLM models and prompt engineering.
• Develop and communicate descriptive, diagnostic, predictive, and prescriptive insights/algorithms.
• Use machine learning and statistical modeling techniques to improve product/system performance.
• Translate algorithms and technical specifications into code using current programming languages.
• Perform testing and debugging of programming implementations.
• Document procedures for installation and maintenance.
• Apply deep learning technologies for complex situation responses.
• Adapt machine learning to virtual reality, augmented reality, and robotics.
• Work with large scale computing frameworks and data analysis systems.
• Design and implement cloud solutions and build MLOps on cloud platforms.
• Build CI/CD pipelines orchestration using tools like GitLab CI, GitHub Actions, or Airflow.
• Conduct data science model reviews, refactoring, optimization, and deployment.
• Test and validate data science models and automate tests.
• Communicate with a team of data scientists, data engineers, and architects.

Benefits

• Medical/Dental/Vision/Life Insurance
• Paid holidays plus Paid Time Off
• 401(k) plan and contributions
• Long-term/Short-term Disability
• Paid Parental Leave
• Employee Stock Purchase Plan

What This Job Offers

• Industry: Professional, Scientific, and Technical Services