Staff Backend / Product Engineer - FinOps & AI Cost Intelligence Platform
Virtasant (virtasant.com) · Austin, US ·
- Work mode
- Remote
- Seniority
- Staff
- Employment
- Full time
- Category
- Backend
- Experience
- 8+ years
- Visa sponsorship
- Yes
Virtasant (virtasant.com) · Austin, US ·
Location: Remote
Type: Full-time
Team: Cost Optimisation (CO) – Product Engineering
Reports to: Director of Engineering
Virtasant is a global technology services company that delivers outcomes through automation. Our services include software engineering, technology operations, cloud migration, application modernization, and cloud optimization.
We help some of the world’s largest organizations modernize their technology operations, optimize costs, and unlock new opportunities for innovation. Our fully remote, globally distributed team is passionate about delivering world-class technology solutions while embracing a culture of excellence, ownership, and impact.
We’re looking for a Staff-level, backend-first Product Engineer to help evolve our multi-cloud FinOps platform into a broader cloud and AI cost intelligence platform. The role will focus on distributed data systems, reliable processing of cloud billing and usage data, platform architecture, and extending AI cost visibility from aggregate spend toward application, workflow and request-level attribution.
You’ll operate with high autonomy, significant ownership, and direct access to product leadership. Think founding engineer energy, without the chaos.
You will help define how AI capabilities move from experimentation to durable product features, with an emphasis on reliability, cost efficiency, and clear user value - not just model novelty.
Design and build backend-heavy platform features for our platform.
Productionalise AI-enabled capabilities (e.g. anomaly detection, recommendations, agent-based workflows).
Implement AI thoughtfully across the entire SDLC - prototyping, testing, iteration, and deployment.
Design and build distributed data pipelines that process cloud billing, usage, and AI telemetry.
Build reliable systems that handle backfills, late-arriving data, and historical reprocessing.
Design scalable data models and APIs that power customer-facing analytics and AI cost insights.
Collaborate closely with Product to turn vision into shipped features.
Identify blockers early, communicate clearly, and iterate fast.
Help shape engineering standards and patterns as the product matures.
You will help define how AI capabilities move from experimentation to durable product features, with an emphasis on reliability, cost efficiency, and clear user value - not just model novelty.
Build AI features with explicit evaluation criteria, feedback loops, and guardrails (accuracy, latency, cost, and explainability) so models improve predictably over time.
Success in the first 6–12 months looks like:
2+ production-ready features shipped.
Tangible progress towards operating as a smart intelligence platform.
Clear, repeatable engineering patterns for AI-enabled development.
Utilize lightweight but rigorous AI engineering practices (evaluation harnesses, rollout strategies, and rollback mechanisms) that allow the platform to scale AI features safely and repeatedly.
8+ years of professional software engineering experience, with deep backend expertise in Python (Java or C++ as secondary languages).
Experience building and operating data-intensive backend systems or pipelines in production.
Strong understanding of data modelling, reliability, and data processing.
Ability to design scalable systems and take them from concept through production.
Experience with AI driven development to accelerate and drive product development.
Hands-on experience building on AWS.
Demonstrated experience using AI in real production systems (not just experimentation - clear, repeatable patterns).
Comfortable working in ambiguity with product-led direction.
Ability to architect backend services that support asynchronous workflows, event-driven pipelines, and AI agents that operate over time rather than single request/response cycles.
Comfort articulating why certain AI approaches were not used, including trade-offs around latency, explainability, data availability, or long-term maintainability.
What Matters More Than Checklists
We care deeply about how you think and build, not just what tools you’ve used.
We’re looking for engineers who can:
Tell a compelling story about a product journey, not just features shipped
Explain why decisions were made and what trade-offs were considered
Fail fast, learn quickly, and iterate relentlessly
Clearly articulate technical roadblocks and collaborate on solutions
Thrive in a fast-paced, high-ownership environment
You’ll work on a real AI product, not internal tooling or demos.
Near-founding-engineer level autonomy and influence.
Direct impact on product direction and commercial outcomes.
Opportunity to help shape a platform with standalone, licensable AI capabilities.
A rare chance to build product inside a consultancy without being consumed by client work.
You’ll build AI capabilities informed by real enterprise-scale cost and usage data, enabling smarter models and workflows than greenfield or synthetic-data products.
High ownership, high trust environment.
Opportunity to own and shape technical delivery at scale.
Work closely with experienced engineering and delivery teams.
Exposure to broader cloud optimisation and consulting initiatives over time.