• Java programming (versions 8–21) • Development of REST applications and services • Communication protocols (REST, SOAP and Kafka) • Architecture and development best practices (Design Patterns, SOLID, DDD, Hexagonal Architecture, etc.) • Kubernetes and the Spring ecosystem (Spring Boot, Spring Data, Spring Security, Spring REST) • Concepts of unit testing, end-to-end (E2E) testing, microservices and distributed architecture • Observability • Differentials: • Java (6, 7) • Java EE and WebLogic • Integrated testing • WebFlux • Dynatrace

• Support Quote-to-Cash processes, including: • Supporting CPQ workflows to ensure deals are configured and quoted correctly • Reviewing order forms and amendments for accuracy and compliance • Provisioning customer contracts, subscriptions, and usage entitlements into internal systems • Support billing, invoicing, and monetization workflows in partnership with Finance • Reconcile customer usage, billing, and revenue data across CRM, product, and finance systems • Serve as a hands-on Salesforce administrator: • Build and maintain reports and dashboards • Support flows, automations, and system enhancements • Help maintain data quality and governance standards • Triage, prioritize, and execute a wide range of RevOps requests • Proactively identify inefficiencies and manual processes and help implement improvements • Leverage AI tools and automation to increase operational efficiency • Contribute to documentation and scalable RevOps processes as the company grows

• Design and build AI-driven features for our domain services platform using Python and Golang. • Integrate and fine-tune open-source models with cutting-edge architectures via tools like Ollama. • Research, evaluate, and implement emerging AI technologies that align with our vision for smarter, more intuitive products and services. • Collaborate with internal stakeholders and fellow engineers to rapidly prototype and iterate on machine learning and LLM-based features. • Contribute to a modern AI development stack, ensuring scalability, performance, and ethical usage of models. • Actively participate in the open-source ecosystem and bring relevant tools and techniques back to the team. • Implement software solutions with “intelligent” features, helping teams automate tedious and repetitive tasks.

• Design and implement scalable AI-driven features • Own the full lifecycle of AI services • Write clean, maintainable code primarily in Python • Establish standards for data privacy, model bias, and security