Staff Backend / Product Engineer - FinOps & AI Cost Intelligence Platform

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Backend / Product Engineer - FinOps & AI Cost Intelligence Platform based in United States. This is a high-ownership, backend-focused product engineering role at the intersection of cloud economics, distributed systems, and artificial intelligence.

You’ll help evolve a multi-cloud FinOps platform into an intelligent product capable of delivering granular cloud and AI cost insights.

Your work will span large-scale billing and usage data, reliable processing pipelines, scalable APIs, and production-grade AI capabilities.

You’ll have significant autonomy and direct access to product leadership, helping shape both architecture and product direction.

The role calls for an engineer who can turn AI experimentation into durable features with measurable value, reliability, and appropriate guardrails.

You’ll collaborate closely with product and engineering teams in a fast-moving, globally distributed environment where technical judgment matters.

This is an opportunity to operate with near-founding-engineer influence while building a real commercial AI product.

Accountabilities

• 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.
• Partner closely with Product to translate product vision into practical, production-ready features.
• Identify technical blockers early, communicate trade-offs clearly, and iterate rapidly toward solutions.
• Establish repeatable engineering patterns, standards, evaluation harnesses, rollout strategies, and rollback mechanisms for AI-enabled development.
• Help shape the technical architecture and engineering practices as the product and AI capabilities mature.
• Deliver meaningful product outcomes, with an initial expectation of shipping at least two production-ready features within the first 6–12 months.

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 rather than experimentation alone.
• 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 and articulate why specific solutions were selected or rejected based on latency, explainability, data availability, cost, reliability, and maintainability.
• Strong communication and collaboration skills, with the ability to clearly explain technical roadblocks, decisions, and trade-offs.
• Demonstrated ability to learn quickly, fail fast, iterate continuously, and thrive in a high-ownership environment.
• Product-oriented mindset with the ability to describe not only what was built, but why decisions were made and how the work contributed to …