• Provide technical leadership for the deployment of a new Digital CORE - Product Lifecycle Management (PLM) system across all GEHC Segments • Define and manage system-level requirements, aligning cross-segment teams to ensure business and technical needs are captured • Develop integration and test plans, resolving technical conflicts through expert guidance • Create efficient new standard processes while maximizing off-the-shelf capabilities of the future PLM tool • Collaborate with engineering, IT, product management, operations, and executive leadership to define requirements, manage conflicts, and drive consensus • Lead workshops to map current and future-state processes, ensuring PLM workflows reflect standardized practices across functions • Lead transformation of CAD integration and data management within PLM to eliminate manual handoffs • Drive item management and BOM transformation for EBOM–MBOM alignment • Lead the development and capture of standardized work processes across existing PLM system functional areas leveraging pre-existing PLM capabilities • Develop integration plans for connecting the PLM system with other enterprise platforms (e.g., ERP, CAD, MES)

• Assess target edge hardware against the requirements of an on-device inference loop: evaluate processor architecture, available memory, OS and runtime environment, and whether candidate edge runtimes (such as IoT Greengrass or equivalent) can be supported. • Evaluate candidate edge inference frameworks for CPU-only SLM deployment — including TensorFlow Lite, ONNX Runtime, llama.cpp, and equivalents — assessing quantization approaches, inference latency, and memory footprint against feasibility targets confirmed during discovery. • Assess real-time data ingestion feasibility from operational subsystem interfaces, evaluating candidate patterns for consuming concurrent data streams within the memory and compute constraints of the target hardware. • Design and evaluate local data store options for the on-device SLM context, including storage formats, retrieval latency, and update mechanisms appropriate for the edge environment. • Build a constrained feasibility demonstrator on laptop or workstation hardware using simulated data feeds. • Implement a small number of scoped interaction flows in the demonstrator, integrating the voice interface pipeline with the SLM inference and local data retrieval components as agreed through the engagement scope. • Collaborate with the AI/ML Architect on SLM selection, domain restriction approach, and inference pipeline design — providing hardware and runtime constraint inputs that shape what is architecturally feasible. • Collaborate with the AWS Solutions Architect on the edge-to-cloud data channel, identifying what can realistically be buffered and transmitted from a constrained edge device under variable connectivity conditions. • Document hardware assessment findings, framework evaluations, and architectural trade-offs as Architecture Decision Records (ADRs) with explicit rationale. • Clearly flag where recommendations are conditional on hardware or interface specifications not yet confirmed. • Communicate technical constraints and feasibility findings clearly to both technical architects and non-technical client stakeholders throughout the engagement.

• Configure and maintain portal bot automation systems using Typescript and Node.js • Manage and optimize Monarch setup configurations for document conversion • Design and troubleshoot advanced Monarch conversion rules • Oversee automated claim backup retrieval processes • Maintain browser automation scripts using Selenium and Playwright • Manage PostgreSQL database operations and automated document processing

• Own and evolve Lime’s Atlassian platform as a scalable, well architected system supporting critical business workflows. • Assess and improve platform architecture, configurations, workflows, permissions, and data structures to reduce complexity, improve usability, and enable long-term scalability. • Define and implement practical governance standards for platform structure, access management, configuration strategy, and operational best practices while balancing consistency with team flexibility and compliance requirements. • Lead hands-on engineering work across Atlassian products and connected systems, including configuration, automation, integrations, migrations, and platform optimization. • Partner with stakeholders to understand business processes, translate needs into effective system design, and guide technical tradeoffs and prioritization decisions. • Create and maintain high quality documentation, admin playbooks, runbooks, and standard patterns so the platform is easier to support and scale over time. • Act as the escalation point for complex platform issues and as a technical leader for Atlassian strategy, platform maturity, and knowledge transfer to internal partners and future team members. • Participate in the team's on-call rotation, responding to incidents, troubleshooting issues, and contributing to root cause analysis and service reliability improvements.