Backend + AI Engineer

Role Summary We are hiring a Backend + AI Engineer to build robust PC-side software for device communications and telemetry-driven intelligence. This role focuses on reliable instrument connectivity, protocol handling, data logging pipelines, and practical AI/ML capabilities such as anomaly detection and predictive alerts—delivered with strong safety and quality guardrails.

Key Responsibilities Build and maintain backend services in C#/.NET to communicate with instruments over serial/COM interfaces. Implement protocol handling: buffering, framing, parsing, validation, checksums/CRC, timeouts, retries, and connection lifecycle. Develop command/control workflows with safe behavior: configuration management, start/stop execution, runtime parameter updates, and fault recovery. Create observability and diagnostics: structured logging, raw TX/RX capture, protocol tracing, and replay tools for reproducible debugging. Build telemetry pipelines: time-series logging, metadata capture, traceability, and data quality checks. Implement practical AI/ML features using telemetry data (anomaly detection, predictive warnings, recommendation-style guidance), with monitoring for drift and performance. Collaborate closely with firmware and hardware stakeholders to validate end-to-end behavior and reliability.

Required Qualifications Strong professional experience with C#/.NET; WPF experience required (MVVM preferred). Demonstrated experience with serial communication and real-world troubleshooting of device connectivity/data integrity issues. Strong concurrency and async fundamentals (thread safety, cancellation, producer/consumer patterns). Strong testing discipline (unit/integration testing, protocol simulators/mocks) and root-cause debugging ability. Working knowledge of ML/AI for time-series or sensor data, with a production mindset (evaluation, monitoring, guardrails). Strong technical documentation skills.

Preferred / Nice to Have Experience with industrial automation, instrumentation software, or hardware-adjacent systems. Familiarity with signal processing basics for noisy sensor streams. Experience with local model inference, model versioning, and ML lifecycle practices. Qt exposure (bonus).

What We Look For A reliability-first engineer who can own ambiguous device-integration problems and drive them to stable outcomes. Pragmatic AI delivery: measurable value, controlled behavior, and maintainable implementation.