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Platform Science is an open IoT platform founded in 2015 that connects fleets, app developers, vehicle manufacturers, and equipment suppliers in the transportation sector. We work with smart cameras and fleet management software that monitors driver behavior, detects fatigue, configures events and alerts, and manages logistics. Our products generate significant data volumes and depend on integrations with customer systems, internal tools, and third-party platforms.
We are seeking an Automation Engineer with a solid software development foundation and the ability to connect systems and transform manual processes into automated, reliable workflows. You will be responsible for building and sustaining the integration and automation layer—including flows that leverage AI models as part of the solution. This is a true engineering role: code goes into the repository, passes review, includes tests, and runs in production with observability. It is not a no-code tool configuration position.
Key Responsibilities:
- Build and operate data pipelines (ETL/ELT): ingest, transform, validate data quality, and make data available for analytical and operational consumption.
- Develop flows that use LLMs and AI models—classification, structured information extraction, summarization, and agents with tool use—including evaluation of model output quality.
- Define and implement error handling, monitoring, logs, and metrics for everything deployed to production.
- Document integration contracts, architecture decisions, and operational runbooks.
- Ensure compliance with information security requirements and LGPD regulations when handling personal data, especially driver data.
Required Qualifications:
- Experience with data pipelines or workflow orchestration tools (Airflow, Dagster, Temporal, Step Functions, n8n, or equivalent). Understanding of DAGs, dependencies, reprocessing, and fault tolerance is more important than knowledge of any specific tool.
- Experience with RAG, vector databases, and systematic evaluation of model outputs.
- Practical experience integrating LLM APIs into applications—structured calls, function calling, handling non-deterministic responses.
Desirable Qualifications:
- Experience with dbt, Kafka, or other event streaming solutions.
- Python proficiency.
- Background in telemetry, IoT, sensor data, or embedded systems.
- Knowledge of BI tools (Power BI, DOMO) from the data production side.
- Infrastructure as Code experience (Terraform, CDK).
- Prior experience with LGPD or GDPR requirements in data products.
