Job Description

When you join us at Thermo Fisher Scientific, you’ll be part of smart, driven team that shares passion for data exploration and discovery. With revenues of more than $40 billion and the largest investment in R&D in the industry. Our Mission is to enable our customers to make the world healthier, cleaner and safer. Whether our customers are accelerating life sciences research, solving complex analytical challenges, improving patient diagnostics and therapies or increasing productivity in their laboratories, we are here to support them. We give our people the resources and opportunities to make significant contributions to the world. Location/Division Specific Information MLOps Team is part of R&D AI Engineering. Team is responsible for establishing MLOps standards, providing infrastructure and development of MLOps Framework for R&D teams of Thermo Fisher Scientific. How will you make an impact? Enabling our R&D teams at scaled, Sr Staff MLOps Engineer will join our R&D AI Engineering team and will be responsible for working with R&D Data Science teams to develop Feature Engineering, Feature Store, Model Management, Model Monitoring, Model Quality and managing staged pipelines across AWS environments and On Premise. Sr Staff MLOps Engineer must be able to collaboratively work in an Agile team to design, develop and maintain MLOps frameworks on Cloud native technology stack. This position offers an exciting opportunity to work on processes that interface with multiple internal teams. The candidate will help contribute and propose scalable design for development projects, pilots, and advance best practices. What will you do? You're a hands-on software developer who wants to make a difference! You work on complex problems that actively enhance our end users' experiences, drive growth in a dynamic company, and use analytics and MLOps technologies. Work on MLOps tools: Work with MLOps team on MLOps frameworks, DataOps tools which can be integrated to ML Platforms for R&D data science teams and AI Engineering team. Work with R&D divisions to get them onboard to MLOps frameworks by supporting their use cases. Setting up GPU farms for R&D data science projects. Providing support for feature engineering and feature stores. Providing best practises and running proof-of-concepts for automated and efficient model operations on a large scale. Creating and maintaining scalable MLOps frameworks to support R&D data science teams models. Collaborate with specialists in other teams (like biologist, chemists, data scientists, etc.) to support R&D workflows. Provide support for small and large projects including analysis, querying, coding, visualization, modelling, and deployment. Good understanding of the machine learning frameworks like(Keras, PyTorch, Tensorflow) used in the model development. Education: Bachelor’s degree in computer science, computer engineering, information systems, or related field, with 12+ years of Experience. Master's degree preferred. Experience: Experience in developing data engineering, CI/CD pipelines, developing MLOps pipelines. Experience in model management, model building and model monitoring. Hands-on experience with ML frameworks, libraries, agile environments and deploying machine learning solutions using DevOps principles is quite high. Experience in Model validation, model training, and other aspects of evaluating an ML system are in addition to traditional code tests like unit and integration testing. Experience with Docker and Kubernetes. Experience in using popular MLOps frameworks like Kubeflow, MLFlow, DataRobot, DVC. Knowledge, Skills, Abilities: Experience on cloud ML solutions like Sagemaker. Experience with Amazon Web Services or other leading cloud deployment platforms and services. Ability to build MLOps pipelines. Strong knowledge on DataOps technologies. Experience on K8’S. Experience with life science solutions that manage and use genomic or biomedical data. Understanding of PCR and CE technologies is a big plus. Understanding about ML Algorithms. Experience working with geographically distributed team. Experience with ML compute and ML model management platforms. Knowledge of frameworks such as Keras, PyTorch, Tensorflow. Ability to understand tools used by data scientists. Excellent oral and written communication skills to present technical information to both business and technology teams with clarity and precision.