Role summary

Key responsibilities

Build modern web experiences

• Lead frontend implementation with React, TypeScript, and modern frameworks such as Next.js. Translate complex technical tasks into coherent navigation, responsive layouts, and efficient workflows.
• Build reusable components and apply shared design systems, strong HTML and CSS, accessible interactions, and clear visual hierarchy. Design loading, empty, error, progress, and recovery states as part of the experience.
• Create responsive interfaces for live imagery, measurement overlays, results, and reports. Keep displayed state accurate when data is delayed, a device disconnects, or a long-running task changes status.

Deliver with customers

• Work directly with customers, application engineers, and product leaders to understand operational needs, observe workflows, and define practical acceptance criteria.
• Own scoped capabilities from prototype through integration, pilot deployment, user feedback, and production improvement. Diagnose problems across application boundaries and coordinate resolution with the appropriate owners.
• Turn recurring customer needs into reusable product capabilities, documentation, and deployment practices.

Application and workflow engineering

Connect web applications and backend services

• Develop and integrate C# and ASP.NET Core application services in a .NET 10 environment, including APIs, session state, data access, and asynchronous processing.
• Define and maintain service contracts for commands, progress, results, cancellation, and recovery. Keep browser state consistent with authoritative backend and device state.
• Integrate with existing native camera and processing services through their supported interfaces, partnering with the engineers responsible for those systems.

Engineer agent workflows and execution graphs

• Build tool-using AI workflows that interpret a task, gather context, perform work, evaluate results, and continue toward a defined outcome.
• Design stateful execution graphs with explicit steps, conditional branches, dependencies, and useful parallel work. Preserve case context and progress across interruptions.
• Build evaluation and refinement loops with measurable completion criteria, bounded retries, time and resource limits, and appropriate human decisions. Prevent duplicate actions when a task retries or resumes.
• Keep validated measurements and evidence authoritative. Present findings, uncertainty, missing evidence, and next actions clearly, without treating generated explanations as verified measurements.

Make camera workflows usable and reliable

• Build guided experiences for camera setup, target selection, acquisition, analysis, and reporting. Help users understand the next action while keeping technical complexity manageable.
• Represent capture authorization, acquisition quality, workflow progress, and equipment findings as distinct states. Preserve user control over recording, cancellation, and requests for additional evidence.
• Work within camera-server and hardware constraints. Measure latency, memory, and processing demand; ensure preview and AI workloads respect recording integrity and agreed resource budgets.

Deploy applications with platform engineering

• Prepare applications for company-managed and local Linux deployments, including environments using Arch Linux. Support normal operation without external network access where required.
• Partner on runtime configuration, authentication, application packaging, deployment automation, logging, monitoring, and recovery. Supply the application requirements and verification needed for dependable releases.
• Validate meaningful customer workflows with QA and domain experts, including device disconnection, interrupted analysis, resource contention, offline operation, and report export.

Use agentic development with engineering judgment

• Use coding agents to accelerate implementation, investigation, refactoring, and verification. Define goals and acceptance criteria, provide useful context, review generated changes, and iterate until behavior is demonstrated.
• Keep solutions understandable and maintainable. Review code, document important decisions, and improve shared engineering practices through evidence from delivery and customer use.

Required qualifications

• A record of independently delivering and supporting production web applications, with senior-level ownership of implementation, technical decisions, and customer outcomes.
• Strong React and TypeScript experience, including component architecture, state management, asynchronous data, performance, and modern frontend tooling.
• Strong web design and interaction judgment: semantic HTML, CSS, responsive design, accessibility, design systems, and the ability to turn a complicated workflow into a clear experience.
• Practical C# and modern .NET experience, including contributing backend service code and debugging across frontend and backend boundaries.
• Hands-on experience building tool-using AI agents or multi-step AI workflows. Ability to demonstrate and explain state, branching, loops, evaluation, termination, retries, and recovery in a working implementation.
• Practical experience using AI coding tools while retaining responsibility for architecture, code quality, and verified behavior.
• Comfort deploying and troubleshooting applications on Linux, using logs, configuration, process and network diagnostics, and performance measurements.
• Ability to work directly with users and technical specialists, turn ambiguous needs into scoped work, communicate tradeoffs, and follow through from implementation to adoption.

Preferred experience

• Next.js production applications, including server and client boundaries and self-hosted deployment.
• .NET 10, ASP.NET Core, gRPC or similar service contracts, and streaming or event-driven application interfaces.
• Stateful agent orchestration frameworks, persistent workflows, local model integration, and evaluation of multi-step AI behavior.
• Video, imaging, real-time visualization, device integration, data acquisition, or other applications with significant latency and resource constraints.
• Arch Linux, containers, customer-hosted or air-gapped systems, and collaboration with platform or embedded engineering teams.
• Industrial software, condition monitoring, vibration analysis, or working alongside domain experts to deliver technical products.

What success looks like

• Customers can complete supported workflows with clear guidance, accurate state, and fewer avoidable handoffs or support interventions.
• Web and .NET capabilities work together reliably through normal operation, interruption, cancellation, and recovery.
• Agent workflows produce inspectable results, retain progress, and stop or request input when they cannot responsibly continue.
• Applications remain responsive within the supported deployment environment while protecting acquisition and recording requirements.
• Customer learning improves reusable product capabilities and measurable usability, reliability, and delivery outcomes.