About us:
Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.
We are building Machine Learning Platform to enable MLOPs capabilities to help Data scientists and ML engineers at Target to implement ML solutions at scale. It encompasses building the Featurestore, Model ops, experimentation, iteration, monitoring, explainability, and continuous improvement of the machine learning lifecycle. You will be part of a team building scalable applications by leverage latest technologies. Connect with us if you want to join us in this exiting journey.
Roles and responsibilities:
Build and maintain Machine learning infrastructure that is scalable, reliable and efficient.
Familiar with Google cloud infrastructure and MLOPS
Write highly scalable APIs. Deploy and maintain machine learning models, pipelines and workflows in production environment.
Collaborate with data scientists and software engineers to design and implement machine learning workflows.
Implement monitoring and logging tools to ensure that machine learning models are performing optimally.
Continuously improve the performance, scalability and reliability of machine learning systems.
Work with teams to deploy and manage infrastructure for machine learning services.
Create and maintain technical documentation for machine learning infrastructure and workflows.
Stay up to date with the latest developments in technologies.
Tech stack: GCP cloud skills, Machine learning engineer skills, Python, Microservices, API development Cassandra, Elastic Search, Postgres, Kafka, Docker, CICD, Addon (Java + Spring boot)
Required Skills:
Bachelor's or Master's degree in computer science, engineering or related field.
9+ years of experience in software development, machine learning engineering.
Deep experience with Python, API development, microservices.
Good to have skills in implementing end to end engineering applications using JVM languages. Prefer experience with Java, Spring boot.
Expert in building high-performance APIs.
Experience with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git.
Experience with GCP ML Ops is required.
Good to have some understanding of machine learning concepts and frameworks, deep learning, LLM etc.
Good to have some experience with MLOps platforms such as Kubeflow, MLFlow, Sagemaker etc.
Good to have experience in deploying machine learning models in a production environment.
Good to have experience with data streaming technologies such as Kafka, Kinesis, etc.
Strong analytical and problem-solving skills
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