AI Engineer
Skills
Cloud InfrastructureEnterprise SoftwareEntrepreneurshipFailure ModesInfrastructureMachine LearningMitigation
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Proven experience deploying applied AI systems into production and owning them through real-world usage
- Experience taking LLM-powered applications from prototype to production where accuracy and reliability matter
- Strong background building applied AI systems rather than conducting ML or model research
- Deep understanding of modern agentic AI architectures and workflows
- Strong Python development skills and experience building production-grade AI applications
- Entrepreneurial mindset with the ability to work independently and make architectural decisions
Nice to have
- Ownership mindset
- Entrepreneurial mindset
- Independent decision-making
- Systems thinking
- Reliability focus
- Production orientation
Day to day
- Own the intelligence layer of the platform end to end and shape how AI powers the product.
- Design and build applied AI systems including reasoning learning loops reliability and evaluation frameworks.
- Work on production-grade LLM applications that must perform accurately in real-world logistics operations and be measured by business outcomes.