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

Staff Backend / Product Engineer - Fin Ops & AI Cost Intelligence Platform(AI Platform)

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 Fin Ops 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.

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