DevOps Software Engineer

We are seeking an AI-Augmented Full-Stack & DevOps Engineer to own the end-to-end lifecycle of our cloud platforms, data pipelines, and client-facing interfaces. In this role, you will bridge IoT telemetry ingestion, GIS/spatial mapping dashboards, backend microservices, and continuous cloud deployment.

We place heavy emphasis on AI leverage. We expect our engineers to leverage modern AI coding assistants and autonomous agents (e.g., Cursor, Claude Code, Windsurf, LangGraph, Model Context Protocol) to execute at the speed of a multi-person engineering pod while maintaining strict architectural rigor and production security.

Key Responsibilities1. Full-Stack Application & Spatial Interface Engineering

• Architect, develop, and maintain web dashboards and mobile-responsive applications (React / Next.js / TypeScript / Tailwind CSS).
• Build interactive mapping and spatial visualization interfaces (Mapbox, Leaflet, GIS layers, and 3D point cloud/mesh visualization) to display urban tree health and sensor telemetry.
• Design and implement high-performance, resilient RESTful and WebSocket APIs using Python (FastAPI) or Node.js to stream sensor data, work orders, and risk indices.
• Manage time-series, relational, and spatial databases (PostgreSQL/PostGIS, TimescaleDB, Redis).

1. DevOps, Cloud Infrastructure & IoT Data Ingestion

• Manage cloud environments (AWS / Azure / GCP) using Infrastructure as Code (Terraform / OpenTofu).
• Maintain containerized environments (Docker, Kubernetes) and orchestrate automated CI/CD pipelines (GitHub Actions) with robust security and linting gates.
• Configure and scale telemetry ingestion pipelines supporting LPWAN standards (NB-IoT, LoRaWAN), MQTT brokers, and field datalogger APIs.
• Maintain end-to-end observability, structured logging, and uptime alerts (Grafana, Prometheus, OpenTelemetry).

1. AI Agent & Tooling Integration

• Build, deploy, and refine internal AI agents and workflow pipelines (LangGraph, CrewAI, or lightweight agentic frameworks) for automated telemetry anomaly detection, report drafting, and customer ticket dispatching.
• Integrate Model Context Protocol (MCP) servers and LLM function-calling tools to query spatial databases and system logs autonomously.
• Champion internal AI-assisted development practices (context window management, automated test mocking, prompt templates) across the codebase.

1. Global Client Collaboration & Overseas Missions

• Interface technically with international enterprise clients and municipal partners across North America (Canada) and Europe.
• Participate in scheduled evening overlap windows (e.g., matching Canadian morning business hours) for technical syncs and architectural reviews.
• Represent LeafIoT internationally at global sustainability/tech summits, trade exhibitions, and onsite client deployment workshops.