Job description

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

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