Product engineering
Build the critical parts with senior technical ownership.
Hands-on implementation for backends, integrations, data workflows, automation, AI capabilities, cloud delivery, and edge systems.
Scope
- Rapid vertical slices
- Production backend services
- API and third-party integrations
- Data pipelines
- Cloud and Kubernetes delivery
- IoT and edge workflows
- DevOps and GitOps automation
Deliverables
- production services with tests, observability, and documentation
- integration and data pipeline implementations
- deployment pipelines and infrastructure configuration
- technical ownership through launch
Typical engagement
Build and Integrate
For teams that need senior hands-on implementation, not only advice.
Suggested duration: 4–12+ weeks
Selected experience
AI
ESP32 edge AI acoustic sensing node
Designed and built a field-ready ESP32-S3 edge device that runs acoustic ML inference locally, reports compact LoRaWAN telemetry, captures labelled training data, and gives operators a local web console for deployment and recovery.
- ESP32-S3
- ESP-IDF
- TensorFlow Lite Micro
- LoRaWAN
Product engineering
Loxone entry management integration platform
Designed and built an integration platform that synchronizes reservation and room-occupancy data into a Loxone-powered entry system, using desired-state reconciliation, durable jobs, monitoring, and safe access revocation.
- TypeScript
- Express
- Redis
- BullMQ
- Loxone
Product engineering
IoT and energy data platform
Designed data ingestion and analysis workflows for distributed meters and sensors, combining device integration, time-series data, automation, dashboards, anomaly detection, and AI-assisted insights.
- MQTT
- Time-series DB
- Node.js
- Automation
Solution architecture
Multi-tenant IoT device platform
Built a greenfield IoT platform covering connectivity across cellular and LPWAN networks, device management, ingestion, visualisation, downlink control, and access control, for tens of thousands of devices across multiple tenants on OpenShift.
- MQTT
- Protobuf
- LoRaWAN
- NB-IoT / LTE-M
- OPA
- Kafka
- Redis
- RabbitMQ
- Prometheus
- Zabbix
- OpenShift
FAQ
Can you join an existing team?
Yes. I regularly work inside existing teams — taking ownership of a critical service or seam while pairing with your engineers so the knowledge stays in-house after I leave.
Can you build an MVP?
Yes, with one caveat: it will be a thin, production-shaped vertical slice rather than a throwaway demo. That costs slightly more up front and saves the rewrite later.
Which technologies do you use?
Primarily Node.js and TypeScript on the backend, PostgreSQL/MongoDB/Redis for data, Kafka or MQTT for events, and Kubernetes for delivery. The choice always starts from your constraints — I fit the stack to the problem, not the reverse.
Do you provide ongoing support?
Engagements can include a defined support window after launch, and longer-term retainers are possible where they make sense. Everything is documented and handed over so support is an option, never a dependency.
Can you work under NDA?
Yes. Most engagements are under NDA, which is also why the public case studies here are anonymised.
Curious what AI could actually do for your business?
Bring your questions, including the ones that feel too basic. In 30 minutes we go through how you work today, pick the task with the most to gain, and sketch what testing it would involve. No pitch, no obligation.
Prefer email? hello@nxtinno.com