Position: Software Engineer (Python)
Company: Fitsoftware
Category: Python
Experience required: 5y
English level: upper

**Software Engineer (Python, REMOTE)**
💰 80 000 - 120 000 USD annually on B2B contract
⏳ Full-time permanent contract 
🕦 100% Remotely with flexible working hours
✈️ Unlimited paid time off 
💎 The international insurance company that protects farmers and people from catastrophic  weather conditions

As a Software Engineer you will be in direct contact with the science team and your job will start where the science ends. The day-to-day tasks will require you to take a prototype of a mathematical/AI/etc. model and turn it into a production component. Whether it is a standalone microservice or a part of a larger ETL data pipeline, you will be responsible for the system to be running smoothly in the Kubernetes cluster. Because of the operational aspect, some former experience with Kubernetes is required. In this role, you will learn the science behind the models and engineering which goes into analysis of satellite data.

**Why work with us?**
-100% remotely
-In case needed, well-located office space in Prague
-Unlimited PTO
-Provided money to create home-office
-Health insurance benefit

**Requirements:**
-5+ years of experience as production level Python engineer, pytest, and the rest of the ecosystem
-Fluent in gRPC/REST microservices design/operations
AWS, Kubernetes, Docker, monitoring (Grafana/Prometheus), Opentelemetry
-Strong communication skills (written and oral) in English

**Bonus points:**
-Apache Airflow, Kubeflow for running AI models
-Other programming languages (GO, C/C++)
-Experience with remote sensing data (multi/hyper-spectral, radar/SAR, etc.) or Big Data
-A curious generalist when it comes to software engineering

**Responsibilities:**
-Take over the code from the science team and turn it into production quality service.
-Function as a “glue” engineer between the science team and SRE/DevOps
-Operate microservices, data pipelines and ensure operational reliability
-Optimize model execution to improve efficiency
-Instrument the microservices/components with telemetry data and create alerts
-Create monitoring dashboards in Grafana to understand execution data pipeline and KPIs