AI Engineer

• Leads the evaluation, deployment, and governance of AI-powered tools and services within PCS, including Anthropic, and Copilot for M365; develops adoption frameworks, monitors usage, and ensures responsible AI use aligned with campus policy.
• Researches, consults, prototypes, and develops new and advanced standards-based technology initiatives and problem resolution across multiple systems
• Designs large components and/or coordinates the running, maintaining, and operating of technical systems and infrastructure
• Integrates, designs, reviews, troubleshoots, monitors, and resolves highly complex, strategically significant, and innovative enterprise-wide supported services, systems, networks, and application problems according to established processes and procedures
• Plans and directs staff implementation of complex enterprise technical projects
• Serves as the main expert point of contact with external stakeholders, leadership, vendors, peer institutions, and IT partners regarding high-level system integrations.
• Identifies needs, provides solution options and strategic direction, and communicates issue updates and resolutions

AI Technical Lead

• Design, evolve, and own the enterprise AI governance framework, including use case intake workflows, risk classification standards, approval criteria, policy management, and responses to client RFP inquiries related to AI governance.
• Set the strategic direction for how governance scales with the organization’s growing AI footprint.
• Serve as the primary governance liaison to the Architecture Review Board, establishing review criteria for AI deployments and ensuring governance checkpoints are embedded in the release management lifecycle.
• Partner with Compliance to design and maintain the AI compliance monitoring approach, defining what gets monitored, how exceptions are surfaced, and how remediation is tracked.
• Proactively identify emerging risk patterns and recommend policy adjustments.
• Lead the organization’s response to evolving AI legislation and healthcare specific regulatory requirements by partnering with Legal, translating legal and regulatory developments into operational policies, stakeholder communications, and process updates that keep the organization ahead of compliance obligations.
• Own the enterprise AI use case inventory, including its structure, taxonomy, and lifecycle stages.
• Ensure every use case is accurately classified, tracked through approval, and documented with risk disposition and ownership.
• Define and continuously improve the AI risk assessment methodology, including scoring criteria, workflow design, and integration with the organization’s GRC platform (LogicGate).
• Collaborate with Vendor Management to establish the vendor AI evaluation framework, assessing model documentation for fairness, bias, transparency, and performance claims.
• Design and deliver the quarterly AI governance reporting package, including use case dashboards, risk trend analysis, and executive summaries.
• Present findings and recommendations to leadership, drive accountability for follow through on identified priorities, and use reporting insights to inform program strategy.
• Facilitate AI governance committee meetings and cross functional workgroups, setting agendas, framing decisions, facilitating productive discussion, and documenting outcomes.
• Manage escalation paths across governance tiers and represents the AI governance program in leadership forums and senior stakeholder meetings.
• Collaborate with Learning Services on the enterprise AI adoption and enablement strategy, including training roadmaps, engagement campaigns, usage analytics, and feedback loops.
• Drive measurable adoption outcomes and continuously refine the approach based on user behavior and organizational readiness.
• Collaborate and partner with engineering on proof of concept AI initiatives, including conversational agents and workflow automations, from problem framing through pilot evaluation.
• Other duties as assigned.

Lead, AI Governance – Enablement

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• 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, including building the evidence when the cause is a vendor

Senior Software Engineer – Applied AI

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – 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

• Design and implement enterprise semantic models and certified KPI layers to ensure trusted, reusable business metrics.
• Build AI-safe data abstraction layers that prevent metric recomputation and ensure consistency across analytics and AI use cases.
• Develop and enforce data contracts, metadata standards, and semantic governance frameworks across domains.
• Engineer scalable batch and real-time streaming data pipelines in a modern cloud environment (GCP preferred).
• Collaborate with AI/ML teams to design reliable grounding strategies for AI applications and agents.
• Implement metadata management capabilities including cataloging, lineage, observability, and automated data quality checks.
• Apply DataOps principles including CI/CD, automated testing, and deployment automation for data products.
• Support domain-oriented and microservices-based data architecture patterns.
• Mentor engineers and promote best practices in semantic modeling, governance, and AI-ready platform design.

Senior AI Context Engineer

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Developing cloud-based workflow and interactive AI solutions.
• Testing and validating AI agents to ensure accuracy and reliability.
• Assisting in the management of a cloud-based application platform including utilization, observability and security functions.
• Understanding of data strategy, cybersecurity and risk practices.
• Using AI coding tools to build features more rapidly while making sure the code is secure, reliable, and fits our platform.
• Leads sub-functional team for application development or enhancement.
• Prepares program documentation and training requirements.
• Develops, prepares, or modifies technical specifications for complex projects, system integrations, and upgrades.
• Designs and implements test plans, and prepares systems test data.
• Conducts analysis for the evaluation and selection of vendor software solutions and packages.
• Reviews application design specifications, codes new applications, and makes enhancements to existing applications.
• Assists in the adoption of agentic AI solutions.
• Maintains cloud hosted infrastructure and integrations.
• Reviews application modules for quality assurance and checks compliance with application architecture standards.
• Trains and provides technical guidance to lower level staff.
• Conducts systems analysis, reviews and interprets system requirements, and develops detailed system design specifications for system integration and upgrades.
• Contributes to the development of data structure and systems performance strategies.

AI Innovation Engineer

• Manage a focused portfolio of strategic customer co-innovation accounts.
• Support top-to-top meetings, executive briefings, and customer workshops focused on AI, autonomy, and emerging technologies.
• Present Rockwell's co-innovation model and help customers understand how to move from interest to structured engagement.
• Partner with account teams to define customer goals, commercial relevance, and next steps.
• Define and operationalize a scalable co-innovation framework, including governance cadence across sales, R&D, and services stakeholders.
• Track progress, milestones, and outcomes across the co-innovation portfolio; connect engagements to pipeline and revenue impact.
• Facilitate regular updates, decision points, and escalation paths – maintaining commercial alignment and keeping projects properly scoped against agreed objectives.
• Connect co-innovation activity to pipeline, revenue potential, customer proof points, and repeatable use cases.
• Capture insights from customer engagements and share structured feedback with product, R&D, and commercial teams.
• Identify patterns across accounts that can inform future offerings, messaging, or productization opportunities.
• Turn co-innovation outcomes into content for external events, including Automation Fair and customer showcases.
• Partner with marketing, enablement, and commercial teams to package successful engagements into field-ready examples.
• Bring a growth mindset, a bias toward inclusion, and a commitment to integrity and accountability to every program and every team you touch.

Customer Co-Innovation Lead – AI & Autonomy

LLM Engineer

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
• Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
• Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
• Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
• Support enterprise AI governance, compliance, auditability, and model risk management requirements.
• Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
• Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
• Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
• Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
• Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
• Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
• Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Senior MLOps, Generative AI Engineer

• Identify high-impact opportunities to transform manual processes into intelligent, AI-driven workflows
• Design and build innovative AI-powered tools, agents, and automations to enhance productivity
• Leverage platforms such as ChatGPT and Microsoft Copilot to create scalable, business-ready solutions
• Create reusable templates and frameworks for AI usage across the organization
• Empower colleagues through hands-on training and demos

Applied AI Engineer, Internal Tools

AI Research Scientist

• Build, deploy, and maintain production-grade AI and ML pipelines, backend services, and infrastructure that support Oura's AI Advisor and custom model initiatives
• Design and implement evaluation systems and tooling to measure grounding, reliability, and actionability of LLM and agentic systems in production
• Develop and deploy agentic AI systems in production
• Lead initiatives end to end — from problem definition and research through deployment, monitoring, and iteration
• Partner with Product and Engineering teams to translate data-driven insights into shipped product features
• Build and optimize custom AI solutions through fine-tuning, knowledge distillation, and quantization
• Help build Oura as a leader in AI expertise and infrastructure
• Proactively identify opportunities to improve the product and bring your own ideas to the table

Senior AI Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Lead the end‑to‑end application architecture for IFS Cloud across Projects, FSM, Resource Management, Manufacturing and Finance.
• Define and govern IFS Cloud reference architectures, design patterns, and guardrails, aligned with Evergreen and SaaS operating principles.
• Promote a configuration‑first mindset, minimizing customization and ensuring long‑term maintainability.
• Translate business and operational requirements into scalable, secure, and supportable IFS Cloud solutions.
• Define environment strategies across DEV, TEST, and PROD, supporting secure delivery and operational stability.
• Design extension strategies using standard IFS capabilities (APIs, Business Events, projections, external services).
• Ensure non‑functional requirements, performance, availability, security, and resilience are built into solution designs.
• Architect and enable AI use cases, including embedded IFS AI offerings: Service planning and scheduling optimization, Demand forecasting and anomaly detection
• Design AI‑augmented business processes, promoting AI insights directly into operational workflows.
• Ensure people trust AI recommendations through human-in-the-loop decisioning. Define feedback loops to monitor outcomes and improve AI‑enabled capabilities.
• Architect end-to-end integration solutions between IFS Cloud and upstream CRM platforms, such as lead-to-order, service demand, and contract lifecycle.
• Design event‑triggered integration architectures using IFS Business Events as the primary mechanism for integrations.
• Ensure systems integrate through the common model rather than point‑to‑point mappings, improving scalability and maintainability.
• Promote loose coupling between IFS Cloud and external systems to support Evergreen upgrades and platform evolution.
• Support enterprise reporting, and advanced analytics by ensuring data accuracy and lineage. Collaborate with analytics and data platform teams to align IFS data structures with enterprise BI, AI, and machine learning initiatives.

AI Engineer – Architect, IFS Cloud

• Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers.
• Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines including data ingestion, document processing, and promoting generation.
• Increase model serving layers and high-throughput data processing pipelines, employing advanced libraries to ensure ultra-low latency and efficient resource use.
• Design and implement secure system architectures for all new AI products ensuring compliance with data governance and security policies.
• Understand how to use Terraform for provisioning and manage Azure infrastructure-as-code.
• Support automated and reproducible Continuous Integration/Continuous Deployment (CI/CD) pipelines.

Senior Agentic AI Architect

• Set up the technical infrastructure required for AI systems, including hardware, software, and data storage solutions.
• Design and configure computing environments to support the execution of AI models.
• Optimize AI models and algorithms for efficient execution and scalability.
• Automate AI infrastructures for the data science team.
• Leverage techniques such as model pruning, quantization, and parallel computing to reduce computational requirements and improve performance.
• Integrate AI systems into existing software frameworks or platforms, ensuring compatibility and seamless interaction with other components.
• Define APIs and protocols for communication between AI systems and external services.
• Implement and maintain CI/CD (Continuous Integration and Continuous Delivery) pipelines to automate the deployment, testing, and monitoring of AI systems.
• Ensure rapid and reliable software releases.
• Monitor the performance and health of AI systems, detecting and addressing issues, such as system failures, performance degradation, or data inconsistencies.
• Apply debugging and troubleshooting techniques to resolve technical problems.

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