Senior Software Engineer End-to-End Python Microservices for Large-Scale ML Data

What awaits you/ Job Profile

i Own microservices end to end: API design Python implementation testing deployment on AWS monitoring and day-to-day operations
Build the services behind our large-scale ML data processing which work on petabytes of data and feed machine learning training and evaluation workflows
Design clean versioned and well-documented APIs for data-heavy services that handle high request volumes
Shape the service architecture using domain-driven design and proven architectural patterns
Make sure services stay reliable observable and scalable in production and take part in incident response and root-cause analysis
Mentor other software engineers and help raise engineering standards across the team through code reviews design discussions and best practices

What should you bring along

A track record of taking services from design through to production and then running them
Solid judgement on architecture and trade-offs especially around scalability resilience maintainability and cost
Passion for clean code good design and software craftsmanship
Enjoyment in mentoring and growing other engineers
Active collaboration with the Technical Lead product owners ML engineers and other feature team members
Willingness to raise technical debt and risks early with the leads
Good communication skills with the ability to describe problems and solutions precisely
High-quality deliverables that need few review comments and produce few defects

Must have technical skill

8 years of overall software engineering experience including at least 4 years building and operating production microservices with full end-to-end ownership
Strong Python skills (FastAPI Flask or Django) including async programming typing testing (pytest) and packaging
Strong grounding in software design: domain-driven design clean/hexagonal architecture SOLID and common design patterns
Experience with API design: REST/OpenAPI versioning authentication/authorization and backward compatibility
Experience building services in the data domain such as data APIs ingestion or processing pipelines or high-throughput backends ideally at terabyte-to-petabyte scale
A proven ability to scale services for high request volumes through caching queuing concurrency and performance tuning
Experience with automated testing strategies (unit integration and contract testing) and CI/CD using GitHub Actions or Jenkins
Experience with Docker and deploying containerized services on AWS
Experience working in an Agile environment: principles timeboxing roles and ceremonies

Good to have technical skills

Experience working with large code bases
Experience with TypeScript and some frontend work (React or similar) including building and deploying frontend applications
Experience with ML data pipelines or ML platforms such as dataset management and feature/data processing for training
Hands-on AWS experience: ECS/EKS Lambda API Gateway RDS/DynamoDB S3 SQS/SNS/Kinesis
Experience with observability tools such as CloudWatch Prometheus/Grafana OpenTelemetry or Datadog
Experience with load testing (Locust or k6) and capacity planning

Required Experience:

Manager