AI/ML Engineer

As a Microsoft Azure AI/ML Engineer on the Data Ops Team, you will be responsible for providing and maintaining a robust and scalable machine learning & data platform in Microsoft Azure. This includes configuring and provisioning components based on end-users needs, productionizing machine learning resources and routines, training platform users and advising on best practices, and providing continuous monitoring solutions for platform stability. You will support our IT and data science stakeholders in their efforts to design, deploy, and maintain machine learning models that provide value to many different areas of our client's business - from applications, to marketing, to underwriting. You will collaborate with other IT teams, including architecture, network teams, and DevOps to build solutions that meet the client's needs within our team.

What You'll Be Doing

• Design and build scalable machine learning infrastructure for model management, continuous training and deployment, and serving predictions
• Create and maintain pipelines in Azure DevOps to automate deployment of code and resources (CICD)
• Create and configure appropriate cloud resources to meet the needs of the end users, including Azure Kubernetes Clusters, Machine Learning Workspaces, and compute instances
• Work with platform users to understand requirements and use cases, and to advise on strategy and best practices for use of the platform
• Work with the team to identify opportunities to automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines
• Monitor cloud-based systems and components for availability, performance, reliability, security, and efficiency. Build a system that fulfills non-functional requirements and service level agreements.
• Support processes to monitor ML applications for operational and ML-related issues, compare model inputs between training and inference, and explore model-specific metrics
• Work closely with data scientists, engineers, and product managers to understand platform usage and identify opportunities to create automation and self-service tools to improve manual processes
• Perform all support activities including training and enablement of platform users

Location and Travel Details

This is a remote position with occasional travel (if needed).

Security Clearance and Citizenship Requirements

Must be willing to obtain and maintain a Public Trust Clearance.

Profile of Success

You have at least (5) years of experience as subject matter knowledge and experience designing and implementing AI apps and agents that use Microsoft Azure Cognitive Services, Azure Bot Service, Azure Cognitive Search, and data storage in Azure. In addition, you will be able to recommend solutions that use open-source technologies, understand the components that make up the Azure AI portfolio and the available data storage options, and understand when a custom API should be developed to meet specific requirements.

• Microsoft certification is required - Azure AI Engineer Associate (AI-100)
• Experience with Azure Machine Learning
• Understanding of the Machine Learning Lifecycle and machine learning concepts
• Applied knowledge of CI/CD DevOps principles and Azure ML pipelines
• Demonstrated experience building and scaling end to end machine learning systems
• Demonstrated experience with PowerShell, BASH, or shell scripting
• Strong knowledge of Linux OS and Windows servers
• Applied experience with Python for data manipulation and analysis solutions
• Exposure to containers: Docker, Kubernetes, or OpenShift
• Working Experience with Azure DevOps
• Strong experience with version control (E.g., git) within Azure DevOps
• Experience with monitoring tools (E.g., AppInsights, Splunk)
• Prior experience in applying Agile development methodology
• Experience performing Root Cause Analysis (RCA) for application and infrastructure

Desirable Skills

• Bachelor's Degree or higher in Computer Science / Computer Engineering or related field.
• Experience with Azure Machine Learning
• Experience with machine learning pipelines (Azure ML)
• Experience in Azure DevOps

About AIS

AIS, Dedicated to Our People

AIS employees can spend their entire career at AIS doing challenging, rewarding work and reach their desired level of achievement and responsibility. We offer the opportunity to move up, without the obligation to move out of a position where one excels. We are committed to our employee's success; however, they define it.

It's our dedication to our employees that inspired our leadership to invest in our future and become partially employee-owned through an Employee Stock Ownership Program (ESOP).

Our employees are our greatest strength, and we do all that we can to serve them. We invest in technology as early adopters, allowing us to create transformative and innovative solutions for our customers while exposing our team to cutting edge technology.

We hire outstanding individuals who are committed to curiosity, passionate about emerging technology, and who are excited to find innovative solutions for the biggest tech challenges facing international brands and government agencies today.

We Invest in Individuals Committed to Innovation

AIS is seeking professionals of a certain character and level of excellence. People that we can learn from and that we can help grow to achieve their personal career goals.

We are looking for:

• Smart people with a passion for technology
• Strong technical capabilities with a consultancy mindset
• Close involvement with local technical communities
• A willingness to think outside of the box to provide innovative solutions to clients
• Ability to solve challenging technical business problems
• Self-directed professional

Our Core Values

• Client Success
• Continued Learning and Technical Excellence
• Strong Client Relationships
• Citizenship and Community

EEO Statement

Applied Information Sciences is an Equal Opportunity Employer and does not discriminate on the basis of race, national origin, religion, color, gender, sexual orientation, age, disability, protected veteran status or any other basis covered by law. Employment decisions are based solely on qualifications merit, and business need.