• Build and deploy ~100 transactional and lifecycle emails in Maropost • Develop a reusable, modular component library plus a global header/footer system • Implement Liquid-based dynamic templates with conditional logic • Configure multi-country and multi-language localization • Audit the client's existing Mandrill email inventory and migrate flows to Maropost • Wire up Shopify triggers to Maropost flows • Support a dedicated QA phase covering business logic variability • Document templates, components, and conditional logic for post-launch maintenance

• Develop, and maintain Ceresti’s caregiver-facing iOS and Android applications using Flutter and Dart • Build elegant, accessible, native-feeling UI that works for caregivers of all ages and tech comfort levels • Integrate with our Go and PostgreSQL-backed APIs, including secure authentication, push notifications, and real-time data sync • Manage the full mobile release lifecycle: build pipelines, code signing, beta distribution via TestFlight and Google Play Internal/Closed testing, phased rollouts, and post-release monitoring • Implement robust offline-first behavior, secure local storage of PHI, and graceful handling of flaky networks • Instrument the app for analytics, crash reporting, and performance monitoring, and act on what the data tells you • Champion mobile accessibility in our apps • Participate fully in our Agile process, and collaborate daily with backend, web, QA, design, product, and clinical teammates • Review pull requests, raise the bar on code quality, and mentor other engineers on mobile best practices • Help safeguard PHI on-device and in transit, including biometrics, secure storage, and logging hygiene

• Developing and maintaining software using Java and enterprise system APIs • Actively participating in all stages of the implementation project from development through testing • Developing relationships with clients and supporting them during the project lifecycle • Collaborating with teammates to maximize project efficiency • Documenting methods and lessons learned

• Develop and implement Generative Artificial Intelligence solutions using AWS services, including Amazon Bedrock; • Build and evolve intelligent agents using frameworks such as LangChain and LangGraph; • Develop orchestrated agent workflows, promoting efficient integrations with language models (LLMs); • Implement AI-based applications such as chatbots, virtual assistants, content classification systems, and intelligent document processing; • Develop solutions using RAG (Retrieval-Augmented Generation) for context enrichment and information retrieval; • Model and implement graph-based data structures using Neo4j and the Cypher query language; • Integrate applications and agents using protocols such as MCP (Model Context Protocol); • Participate in the definition and implementation of testing, observability, and monitoring for developed solutions; • Contribute to the continuous evolution of AI application architecture in cloud environments; • Collaborate with multidisciplinary teams to build data- and AI-driven solutions; • Use Git for code versioning, collaboration, and project maintenance; • Support technical documentation and the dissemination of development best practices.