Sr Staff Engineer, Software

Job Description

Purpose:

MLOps Engineer will own Feature Engineering, Feature Store, Management of Model, Model Monitoring, Model Quality and handling staged pipelines across AWS environments and On Premise for AI and R&D teams.

What will you do?

You're a hands-on software developer who wants to make a difference! You work on complex problems that actively improve our end users' experiences, drive growth in a multifaceted company, and use analytics and MLOps technologies.

Work on MLOps tools: Work with MLOps team on MLOps frameworks, DataOps tools which can be coordinated 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 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.

Crafting and maintaining scalable MLOps frameworks to support data science team 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 validated 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.

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 Sagemaker.
• Ability to build MLOps pipelines.
• Working Experience on 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.