MLOps Engineer

"Job Summary:

We re looking for a GCP ML/MLOps Engineering Specialist who is experienced in designing and implementing ML applications at scale in production for our ML Engineering team. The team is a cross-functional team and has ML Engineers and Software Engineers, and closely works with data scientists and data engineers in designing, building and operationalizing AI/ML models.

Roles and Responsibilities:

As a ML Engineering Specialist, you will be a key stakeholder and owning responsibility in designing and architecting end to end ML solutions.

Operationalize ML models, NLP, Computer Vision and other type of models.

End to End model lifecycle management, starting from feature extraction to monitor machine learning models using high end tools and technologies.

Design & implementation of DevOps principles in Machine Learning

Model quality assurance, governance, and monitoring

Integrate models as part of business applications via APIs.

Execute best practices in version control and continuous integration / delivery.

Collaborate with data scientists, engineers, and other key stakeholders.

Work well in a fast-paced cross-functional environment

Mandatory Skills/Requirements:

Experience in implementing machine learning life cycle one of Vertex AI along with other GCP services.

Experience with Machine learning frameworks, libraries, and agile environments.

Experience implementing GCP AI services in business applications.

Experience with various model deployment strategies

Experience with Python and SQL is must. Understanding of distributed frameworks such as Spark, Dask, Ray etc. is a plus.

Experience with version control tools such as Git, Bitbucket etc.

Knowledge on Docker, Jenkins, Kubernetes, and other DevOps tools.

Knowledge on Infra-as-a-Code via ARM or Terraform templates.

Familiarity with Large Language Models and Operationalization of foundation models on cloud platforms will be a plus.

Outstanding analytical and problem-solving skills."