Staff Backend / Product Engineer - FinOps & AI Cost Intelligence Platform
New
J
JobgetherCloud FinOps
Based in United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSBackend DevelopmentPythonArtificial IntelligenceData modelingDistributed Systems
Requirements
- 8+ years of professional software engineering experience, with deep backend expertise in Python.
- Java or C++ experience is a plus.
- Proven experience building and operating data-intensive backend systems or production data pipelines.
- Strong understanding of data modeling, data processing, system reliability, and scalable backend architecture.
- Demonstrated ability to take complex systems from concept and architecture through production deployment and ongoing operation.
- Hands-on AWS experience.
- Demonstrated experience using AI in production environments, with clear and repeatable engineering practices.
- Experience using AI-driven development techniques to accelerate product and software engineering work.
- Ability to architect systems supporting asynchronous workflows, event-driven processing, and AI agents that operate over time.
- Strong product mindset and comfort working with ambiguous, product-led direction.
- Ability to evaluate AI approaches critically based on latency, explainability, data availability, cost, reliability, and maintainability.
Responsibilities
- Design and build backend-heavy product features that expand the FinOps and AI cost intelligence platform.
- Architect and operate distributed data pipelines processing cloud billing, usage, and AI telemetry at scale.
- Build reliable data-processing systems capable of handling backfills, late-arriving data, historical reprocessing, and evolving data requirements.
- Develop scalable data models and APIs that power customer-facing analytics and AI-driven cost insights.
- Productionize AI-enabled capabilities such as anomaly detection, recommendations, and agent-based workflows.
- Apply AI throughout the software development lifecycle, including prototyping, testing, iteration, deployment, and ongoing improvement.
- Build AI features with explicit evaluation criteria, feedback loops, and guardrails covering accuracy, latency, cost, explainability, and reliability.
- Design asynchronous workflows, event-driven pipelines, and backend services capable of supporting AI agents operating over extended workflows.
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