• Responsible for designing, implementing, and maintaining scalable backend services primarily in Python • Building and managing data pipelines for AI/ML applications • Operationalizing and deploying Generative AI systems for production use • Developing APIs to serve AI features to downstream applications • Orchestrating agentic workflows using frameworks like LangChain, LangGraph, or custom solutions • Ensuring monitoring, logging, and observability of GenAI services • Collaborating cross-functionally to ensure system performance, scalability, and reliability

• Own the end-to-end technical architecture of the iOS clinical application • Build the data-rich member experience at the heart of the platform: structured workout delivery and logging (prescribed versus completed), longitudinal progress visualization, assessment results, and educational content • Implement HealthKit and Apple Watch integration and present wearable-derived data — activity, sleep, recovery, heart-rate variability- in a member-friendly, longitudinal format • Partner with backend and web engineers to define the API contracts and shared data models underpinning training programs, assessments, and wearable data • Develop real-time pose estimation and movement-analysis capabilities using VNDetectHumanBodyPoseRequest, AVFoundation, and CoreML, advancing an existing live prototype to clinical-grade quality • Partner directly with ML/CV engineers on the on-device implementation of custom movement models • Define iOS engineering standards for the clinical team, including code organization, testing strategy, and release processes, building on the conventions of our existing mobile team where applicable • Mentor and review the work of a junior iOS engineer who will join the team shortly after this hire • Collaborate with our existing iOS team of three engineers, who ship our consumer QuadraScan applications, with the authority to evolve shared CI/CD and release infrastructure to meet clinical-grade requirements

• Engineer and sustaining upstream (vanilla) Kubernetes environments that enable reliable, repeatable software delivery environments • Python scripting and Helm-based Kubernetes packaging to automate platform workflows and deployments • Engineer secure-by-default Kubernetes platform capabilities, embedding security controls into automation, delivery workflows, and operational practices • Translate platform capabilities into repeatable PaaS services that accelerate internal delivery and enable customer-targeted outcomes • Operate container runtimes (Docker/Podman) and containerization to support CI/CD execution and cloud-native tooling deployments • Develop secure end-to-end CI/CD pipelines and provide organization-wide CI/CD tooling expertise, guidance, and support • Implement Kubernetes observability (logs, metrics, traces) and embed it into GitOps-driven automation to validate deployments, detect regressions, and accelerate incident response • Partner with development and infrastructure teams to automate deployments and release validation, applying best practices in delivery, testing, security, performance, and disaster recovery • Evaluate and adopt cloud-native OSS/COTS tools to meet platform needs, enable teams through knowledge transfer, and integrate new capabilities into standard workflows

• Model data in Dataverse — design tables, relationships, keys, and option sets; produce entity-relationship diagrams (ERDs) and a clear, well-documented schema that reflects mission requirements. • Migrate legacy data into Dataverse — profile source systems; map, transform, and load legacy data; and validate the results for completeness and accuracy. • Build ETL/ELT pipelines — develop repeatable dataflows using Power Automate, Azure Data Factory (ADF), or comparable tools to ingest, transform, and standardize data. • Integrate external systems — work with REST and OData endpoints and build custom connectors to exchange data between Dataverse and other Army/DoD systems. • Implement Dataverse security — configure security roles, field-level security, and environment access models to enforce least-privilege access and data protection requirements. • Manage solutions and ALM — package and deploy schema and components via managed and unmanaged solutions; maintain a Dev/Test/Prod environment strategy with solution pipelines and version control. • Ensure data quality — clean, deduplicate, and validate migrated and operational data, and establish checks that keep it reliable over time. • Build Power BI semantic models — design semantic/data models on Dataverse and author DAX measures that support operational and leadership reporting. • Translate requirements — convert business and mission needs into concrete data structures, entities, and relationships. • Document the solution — maintain ERDs, data dictionaries, migration plans, and supporting technical documentation. • Collaborate across teams — partner with developers, SMEs, and mission owners to align solutions with mission outcomes and to support adoption.