• Translate strategic objectives into well-defined epics, user stories, and technical acceptance criteria that enable autonomous, high-velocity engineering execution; • Translate strategy into detailed epics, user stories, and technical acceptance criteria that guide engineering execution. • Partner with architects to ensure system designs meet performance, security, and compliance standards across environments. • Lead backlog prioritization to maintain velocity and technical quality. • Act as a hands-on, embedded technical product leader, stepping in where needed across discovery, design, delivery, and troubleshooting to unblock teams, accelerate progress, and ensure alignment across disciplines. • Directly contribute to execution when needed (e.g., prototypes/spikes, debugging, performance tuning, instrumentation/observability, automation/CI/CD improvements) to unblock delivery and accelerate outcomes. • Leverage data instrumentation, analytics, and observability tools to assess product health, performance, and ROI. • Contribute to DevOps practices, CI/CD optimization, and automation efforts to improve release reliability and deployment speed. • Establish cycle time, flow efficiency, and throughput as primary KPIs; accountable for demonstrable improvement across assigned product areas. • Model and institutionalize AI-native product and engineering practices, including AI-assisted discovery, design, coding, testing, and operations. • Influence prioritization, sequencing, and technical investment decisions to optimize flow, balancing feature delivery, platform health, and technical debt reduction to sustain long-term velocity gains. • Partner with software engineering, architecture, and infrastructure leaders to shape technical direction and operating models that remove friction, reduce cycle time, and enable predictable, high-throughput delivery. • Lead technical discovery and product design sessions, serving as subject matter expert (SME), converting complex business and operational needs into well-structured platform capabilities; • Serve as the subject matter expert (SME) for assigned products, fully owning knowledge of functionality, architecture, dependencies, and integration points. • Own outcomes across 2-4 product areas by driving cross-team dependency alignment, surfacing tradeoffs, and ensuring initiatives ship to measurable impact. • Promote hands-on collaboration between Product, Engineering, and Service Delivery, fostering a culture of transparency, experimentation, and continuous learning. • Introduce and train teams on modern product management methodologies, emphasizing discovery-driven development and customer-centric design.

• Act as the technical lead for Microsoft Dynamics 365 CE applications • Design and develop scalable and robust solutions • Build and maintain integrations with external systems and platforms • Ensure high standards of code quality, security, and performance • Contribute to technical architecture and development standards • Perform code reviews and support other developers • Collaborate closely with product owners, consultants, and business teams • Analyze and resolve complex technical issues • Support and improve CI/CD pipelines and engineering processes

• Design, develop, and maintain geometry processing and simulation algorithms for engineering applications. • Build services for reading, processing, and writing 2D/3D engineering data. • Develop rendering modules for generating 2D/3D visual assets. • Curate and manage large-scale datasets for learning-based systems. • Implement and optimize post-training workflows for machine learning models. • Contribute to the development of domain-specific languages for engineering tasks.

• Turn ambiguity into execution — Translate rough ideas into clear product concepts, flows, and working prototypes. • Build fast with AI agents — Use tools like Claude Code, Codex, and similar workflows to ship MVPs, experiments, and production components quickly. • Design AI-native products — Create experiences where AI is core to the product—not just an add-on. • Develop agent workflows — Design systems that combine prompts, tools, retrieval, memory, and orchestration patterns effectively. • Bridge prototype → production — Know when something is “good enough” to validate—and when it needs to be hardened for scale. • Collaborate cross-functionally — Work closely with product, design, and engineering to prioritize high-impact problems and ship quickly. • Build across the stack — Contribute to frontend, backend, integrations, internal tools, and automation as needed. • Ensure reliability — Implement lightweight evals, QA checks, and monitoring for AI-driven systems. • Document and scale learnings — Capture insights, reduce handoff friction, and turn experiments into repeatable patterns.