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Job Type
Full Time
Job Details
Job Description What you get to do in this role: The MLOps Platform Team works within the Enterprise Data and Analytics Organization at ServiceNow driving the ability to democratize machine learning to unleash data driven decisions across the enterprise, helping teams build high-value data and AI/ML products, and enable the operationalization and reliability of all models. We are searching for a driven and highly skilled MLOps Engineer to join our MLOps Platform team at ServiceNow. The role will build the MLOps Platform, build self-service ML Development tooling, and building platform adoption. You have ideas on how to create a great user experience for those building , deploying, and operationalizing production quality Machine Learning models. We have a team of people just as excited. Join us. Responsibilities:
- Help implement scalable and secure architectures, frameworks and pipelines for building, deploying and diagnosing production ML applications
- Writing code, testing, debugging, deploying and providing operational support for the MLOps Platform
- Collaborate with internal team members to build a comprehensive MLOps Platform
- Design and implement cloud-based solutions (e.g., MS Azure)
- Develop documentation, examples, videos to drive platform adoption.
- Create way to automate the testing, validation, and deployment of machine learning models
- 5+ years of related experience with a Bachelor's degree, Masters degree or PhD or equivalent work experience.
- 5+ years of experience working with an object-oriented programming language (Scala, Python, Java, C/C++ etc.)
- Proficiency in programming (Python, R, SQL)
- Ability to implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., MS Azure)
- Strong understanding of DevOps principles and practices, CI/CD, etc. and tools (Git, GitHub, jFrog Artifactory, Cloudbees/Jenkins, Airflow, etc.)
- Experience with containerization technologies like Docker and Kubernetes
- Strong communication and collaboration skills
- Ability to work in an Agile manner with a team to write User Stories and Tasks from higher level requirements.
- Masters and/or PHD degree preferred.
- Experience with MLOps frameworks like MLflow, Kubeflow, etc.
- Ability to create model inference systems with advanced deployment methods that integrate with other MLOps components like MLFlow.
- Knowledge of inference systems like Seldon, Kubeflow, etc.
- Knowledge of deploying applications and systems in Kubernetes using Helm and Helmfile.
- Knowledge of infrastructure orchestration using Terragrunt and Terraform
- Exposure to enterprise feature stores (such as Feathr, Feast, Tecton, etc.)
- Exposure to observability tools (such as Evidently AI, WhyLogs, etc.)
About the Company
ServiceNow
Santa Clara, CA, United States
At ServiceNow, our technology makes the world work for everyone, and our people make it possible. We deliver digital workflows that create great... Read more