• Design and develop large-scale, single- and multi-tenant, robust, cutting-edge Java-based identity management products • Collaborate effectively with the distributed team, with team members in the Portland, OR metro area, Canada, and Europe • Analyze and elaborate on requirements with product management and architects to improve the identity platform and develop new features for PingIDM • Plan, estimate, and manage development tasks independently and efficiently per business plans • Enable successful cloud deployments • Work closely with customer-facing support teams to resolve customer issues

• Drive technical direction for a significant product domain or platform capability, ensuring alignment with company goals and customer needs • Reduce systemic complexity by identifying and leading architectural debt remediation efforts, improving overall system reliability and maintainability • Partner with Product and Engineering leadership to assess multi-quarter roadmap feasibility, providing technical guidance and recommendations • Elevate engineering craft across multiple teams through Request for Comments (RFCs) and mentorship, promoting best practices and knowledge sharing • Collaborate with cross-functional and cross-country teams to design and implement scalable, high-quality software solutions, ensuring timely delivery and customer satisfaction • Shape how the team reviews AI-generated code and supervises AI agents, and adapt architecture so codebases are ready for agentic contribution • Own cookbook and knowledge-base standards that make institutional knowledge and best practices consumable by both engineers and AI agents • Evaluate and help standardize agentic development tools across engineering, balancing velocity gains against cost, security, and quality risk

• Build and evolve the internal AI platform — a shared layer (model routing, prompt versioning, tools registry, memory, observability, and cost) separated from each area’s solution, with a paved path and self-service for tech and non-tech teams. • Own end-to-end architecture decisions for agentic/agent-based systems: orchestration, state, idempotency, retry/durability, RAG, model selection and routing, build vs. buy — planning, executing, and observing. • Measure the effectiveness of AI solutions (RAG effectiveness, agent reliability, hallucination rate) using evaluation and regression datasets, and use the numbers to decide — not just to monitor. • Share knowledge and raise the company’s AI bar: provide trainings, materials, and scalable best practices that extend usage beyond the team, translating non-technical area needs into solutions. • Participate in strategic projects hands-on, coding high-impact deliverables on short timelines when that moves a meaningful business metric. • Make and document architecture decisions with clear trade-offs, cost estimates, and impact. • Promote a culture of observability, security, and performance in AI solutions.

• Build and ship end-to-end product experiences across modern web applications, owning delivery from early concept through production release and iteration. • Rapidly prototype new concepts, validate feasibility and user value, and graduate successful prototypes into secure, scalable, maintainable production services for the first set of customers. • Design and manage AI-powered capabilities across retrieval, context shaping, and personalization (RAG, context engineering, collaborative filtering, content-based filtering, etc.), balancing quality, safety, privacy, and latency. • Build or leverage API-first, versioned REST microservices that expose domain capabilities in a consistent, reliable way across experiences. • Apply AI agents and modern developer tooling to speed up development workflows, including scaffolding, code generation, evaluation, debugging, and test automation. • Establish engineering clarity and alignment through strong technical communication and documentation (e.g., architecture briefs, API specs, runbooks, decision records). • Champion pragmatic software quality practices: code reviews, automated testing, CI/CD, observability, performance tuning, and cloud cost-aware design. • Partner closely with Product, Design, Research, Data Science, and customer-facing teams to translate needs into working software that meets business outcomes. • Ensure regulatory, compliance, security, and privacy expectations are met throughout development and release. • Independently create a test strategy (unit/integration/e2e) aligned to risk, including security testing. • Write architecture briefs and proposals (lightweight but clear), run experiments, and document tradeoffs. • Integrate telemetry for performance, reliability, availability, and usage insights. • Perform other duties as assigned.