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.