Senior Systems Architect

Lutra

Vancouver, British Columbia, Canada

Vancouver, British Columbia, Canada

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À propos

Your Opportunity
This role sits within a climate technology company operating at the intersection of atmospheric science, machine learning, and large-scale data systems. The organization builds cloud-native platforms that ingest, process, and model atmospheric, environmental, and sensor-driven data to predict and reduce wildfire risk before ignition occurs.
You will design and evolve the core cloud architecture that enables machine learning models and scientific research to move reliably from experimentation into production. The platform is intentionally layered, separating data storage, processing, services, and visualization so that new data sources, models, and analytical workflows can be introduced with minimal coupling. As the platform scales, your work will ensure that machine learning outputs and data products can be orchestrated end to end, exposed through APIs, and consumed by frontend applications, including map-based and geospatial interfaces, in a secure, observable, and extensible way.
Key Responsibilities

Architecture Design: Design and maintain a modular, layered cloud architecture that separates data storage, processing, services, and frontend consumption

Abstraction Patterns: Define interfaces, adapters, and schemas that enable plug-and-play data sources, machine learning pipelines, and API services

Containerized Processing: Build and operate containerized data and ML processing workloads with a focus on portability, scalability, and reproducibility

Workflow Orchestration: Design and maintain orchestration for end-to-end workflows, including scheduling, dependencies, retries, and monitoring

API Services: Develop and guide Python-based API services that expose data products and model outputs securely and at scale

Cloud Services Integration: Select and integrate managed services across major cloud providers to support storage, compute, networking, and security

Platform Practices: Establish and uphold best practices for CI/CD, infrastructure as code, observability, security, and cost management

Cross-Functional Collaboration: Partner with data, machine learning, backend, and frontend teams to translate platform requirements into clean, scalable technical solutions

Tech Stack

Languages: Python

Containers: Docker

Orchestration: Kubernetes or managed workflow services

APIs: FastAPI, Flask, Django

Cloud Platforms: AWS, Google Cloud, Microsoft Azure

Your Know-How

You have strong experience designing and building cloud architectures and backend systems using Python

You have hands-on experience running containerized workloads in production environments.

You have experience orchestrating container-based workflows at scale

You have designed layered, abstracted systems with clear separation of concerns

You understand databases, object storage, batch and streaming data patterns, and ML-adjacent data workflows

You have experience supporting frontend data consumption, including visualization or mapping use cases

It's a Bonus If

You have experience working with geospatial data formats or geospatial services

You have a background in platform engineering or multi-cloud environments

You are coming from a Machine Learning Operations background

Vancouver, British Columbia, Canada

Compétences linguistiques

English

Avis aux utilisateurs

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