Clinical Data ML Ops Engineer

Job Description : Clinical Data ML Ops Engineer

Summary : Experience in AIML / Generative AI other Cloud platforms, as well as experience in natural language processing and simple UI development skills. Details below. 6-8 years of relevant experience in Data Science delivery and team management.

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