AI Engineer

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Work as part of a global Enterprise Workplace & Collaboration engineering team to implement, administer, and support Microsoft 365 services, with a primary focus on Microsoft Purview, Microsoft Copilot services, and agentic AI administration.
• Support the Enterprise Workplace & Collaboration team in delivering Microsoft 365, Purview, Copilot, and AI-enabled services aligned with business, regulatory, and technology requirements.
• Implement, administer, and maintain Microsoft Purview services including information protection, sensitivity labeling, data loss prevention, eDiscovery, audit, and insider risk.
• Support the deployment, configuration, and ongoing administration of Microsoft Copilot services, including access management, security controls, and operational support.
• Assist with the implementation and administration of Copilot extensibility and agentic AI solutions in alignment with enterprise governance standards.
• Participate in problem management, incident resolution, escalation support, and root cause analysis across Microsoft 365, Purview, and Copilot platforms.
• Collaborate with engineering peers, architects, security teams, and business partners to deliver collaboration, compliance, and AI-enabled solutions.
• Contribute to technical documentation, operational procedures, and service improvement initiatives.
• Assist in evaluating new Microsoft 365, Purview, and Copilot capabilities and provide recommendations for adoption and operational readiness.
• Support operational standards, procedures, monitoring, and resiliency practices to maintain service reliability and effectiveness.

Microsoft 365 Engineer, Purview & AI

• The Lead, Network Engineer designs, analyzes, plans and modifies network components supporting customer communication implementation activities.
• Work on problems of diverse scope and complexity ranging from moderate to substantial.
• Be the technical SME for our enterprise WAN, remote LAN/WLAN, and data center SD-WAN network services.
• Responsible for engineering, implementing, and operating a multi-vendor SD-WAN environment, scalable campus/branch switching and wireless, and resilient remote branch solutions.
• Serve as the escalation point for complex routing, overlay, and authentication issues, including RADIUS/TACACS+.
• Provide technical leadership to a team of network engineers.
• Work closely with the Lead Network Architect and Principal Network Architect through structured technical solution peer reviews.

Lead Network Engineer – AI

• Participate in the design, development, testing, and maintenance of software applications and platform solutions.
• Collaborate with senior engineers and stakeholders to gather and analyze business and technical requirements.
• Develop software components using programming, scripting, and database technologies.
• Support software implementation activities, including coding, debugging, testing, and deployment.
• Assist in analyzing software requirements, system functionality, and interactions between hardware and software components.
• Contribute to software enhancement initiatives by implementing new features and resolving defects.
• Participate in code reviews and follow established coding standards and software development best practices.
• Support software testing activities, including unit testing, integration testing, and defect resolution.
• Maintain accurate technical documentation for software functionality, processes, and system changes.
• Work with source control systems, build tools, and development environments to support software delivery.
• Respond to scope changes, troubleshoot issues, and support continuous improvement efforts throughout the software lifecycle.
• Learn and apply modern software engineering methodologies, tools, and development frameworks.

Vision AI Engineer

Machine Learning Engineer

• Collaborate with cross-functional teams including data scientists, software engineers, and business stakeholders to identify opportunities for leveraging machine learning techniques to drive business outcomes.
• Design, develop, and deploy scalable machine learning models and algorithms that address business challenges and improve operational efficiency.
• Optimize machine learning models for performance, scalability, and efficiency.
• Build robust data pipelines and infrastructure to support the training and deployment of machine learning models in production environments.
• Work with DevOps teams to automate deployment processes, monitor system performance, and ensure the smooth operation of applications and services in production.
• Stay updated on emerging technologies and industry trends in machine learning, software engineering, and cloud computing, and evaluate their potential impact on our business operations.

Machine Learning Engineer II

• Support the deployment, monitoring, and ongoing maintenance of media measurement and customer modeling systems in partnership with Data Science and Engineering teams.
• Develop and maintain SageMaker processing and training jobs, model endpoints, and supporting infrastructure across development, testing, and production environments.
• Contribute to Step Functions, Lambda functions, and Airflow (MWAA) workflows that orchestrate model training, scoring, retraining, and analytics pipelines.
• Support MLflow model registration and promotion processes, configuration management, and versioned model artifacts.
• Build and maintain Docker images, ECR repositories, and GitLab CI/CD pipelines to enable reliable model deployment and release processes.
• Help productionize machine learning models and data pipelines that support customer analytics, scoring, and decisioning use cases.
• Investigate and resolve production issues using CloudWatch, DataDog, SageMaker logs, and workflow monitoring tools.
• Collaborate with cross-functional partners to implement platform enhancements, improve operational reliability, and deliver new capabilities.
• Contribute to engineering best practices, documentation, testing strategies, and operational procedures.

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