Staff MLOps Engineer

Company Description

It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

Job Description

What you get to do in this role:

The AI Platform Team works within the Enterprise Data and Analytics Organization at ServiceNow.  We are driving the ability to democratize AI and Machine Learning to unleash the internal organizations within ServiceNow to help teams build high-value AI/ML products, and enable the operationalization and reliability of all AI. We are searching for a driven and highly skilled MLOps Engineer to join our AI Platform team at ServiceNow. The role will help work with teams that use our AI Platform to maximize their chance of success, and help unblock them from a technical and design standpoint.  We view this as an internal solution architect, that can contribute towards the MLOps of the Platform when needed.  Someone that can build and advise.  Someone who can show and not just tell.

Responsibilities:

• Design scalable, secure and maintainable code, architectures, frameworks and pipelines.
• Enable users & teams on the AI Platform; troubleshoot and debug user issues; maintain user-friendly documentation and training.
• Help to collaborate with teams across the business to ensure they maximize their success using the AI Platform.
• Design and implement cloud solutions and build AI pipelines on cloud solutions (e.g., MS Azure)
• Develop standards and examples to accelerate the productivity of data science teams.
• Write code, refactoring, optimizing, containerization, deployment, versioning, and monitoring of its quality, including data & concept drift.

Qualifications

Requirements:

• 8+ years of related experience with a Bachelor's degree, Masters degree or PhD or equivalent work experience.
• 8+ years of experience working with an object-oriented programming language (Scala, Python, Java, C/C++, etc.)
• Ability to design and implement MLOps with Databricks.
• Strong knowledge of Python standard libraries and productionalizing Python libraries and applications (command-line, etc).
• Experience with Git and GitHub
• Experience using diagraming tools (Lucidchart, Draw.io, Vizio), for designing, documenting and discussing technical solutions.
• Strong communication and collaboration skills.
• Ability to mentor engineers and help them become better.
• Ability to help work with a team to create User Stories and Tasks out of higher level requirements.

Preferred:

• Masters and/or PHD degree preferred.
• Experience with MLflow.
• Strong understanding of DevOps principles and practices, CI/CD, etc. and tools (Git, GitHub, jFrog Artifactory, Cloudbees/Jenkins, Terraform, etc.).
• Ability to create model inference systems with advanced deployment methods that allow progress rollouts and automated validation.
• Knowledge of inference systems.
• Experience with containerization technologies like Docker and Kubernetes.
• Knowledge of infrastructure orchestration using Terragrunt and Terraform.
• Exposure to building and using feature stores.
• Exposure to observability applied to AI and ML.

For positions in this location, we offer a base pay of $155,800 - $272,700, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.

Additional Information

Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location.