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Job Title-** **Clinical Data ML Ops Engineer

Location - Hybrid @ North Chicago- 3 days a week

Key responsibilities:

Responsibilities may include the following and other duties may be assigned.

• In new product design roles: develops and programs integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments.

• Well versed with LLM models and prompt engineering

• Develops and communicates descriptive, diagnostic, predictive and prescriptive insights/algorithms.

• In product/systems improvement projects: uses machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy.

• In both theoretical development environments and specific product design, implementation and improvement environments, uses current programming language and technologies to translate algorithms and technical specifications into code.

• Completes programming and implements efficiencies, performs testing and debugging.

• Completes documentation and procedures for installation and maintenance.

• Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations.

• Adapts machine learning to areas such as virtual reality, augmented reality, artificial intelligence, robotics and other products that allow users to have an interactive experience.

• Can work with large scale computing frameworks, data analysis systems and modeling environments.

MLOps:

• Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)

• Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools

• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality

• Data science models testing, validation and tests automation

• Communicate with a team of data scientists, data engineers and architect, document the processes

Required Qualifications/Skills:

• Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP)

• Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift

• Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.

• Ability to understand tools used by data scientist and experience with software development and test automation

• Fluent in English, good communication skills and ability to work in a team.

**Salary and Other Compensation** :

Applications will be accepted until Nov 16th 2025

The annual salary for this position is between $ 115-135K+ depending on experience and other qualifications of the successful candidate.

This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.

**Benefits** : Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:

+ 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

**Disclaimer:** The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.

Cognizant is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.