Virtasant: Senior Backend / Product Engineer (AI Platform)

Senior Backend / Product Engineer
(AI Platform)

Location: Remote
Type: Full-time
Team: Cost Optimisation (CO) – Product Engineering
Reports to: Product Lead (CO)

About Virtasant

Virtasant is a global technology services company with a network of over 4,000 professionals across 130+ countries. We specialise in cloud architecture, engineering, and transformation, helping enterprises deliver at scale while building internal capability and reducing third-party dependency.

We work with ambitious, forward-thinking organisations to solve real-world challenges through hands-on execution, deep technical expertise, and a delivery-led approach.

The Role

We’re hiring a senior backend-first product engineer to help build and scale our AI-powered cost intelligence platform.

This role is explicitly product-focused. You will not be pulled into client delivery. Your mission is to build core platform capabilities that mature our current FinOps platform into a smart intelligence platform - including AI-driven insights, workflows, and agent-based features that can be sold as standalone capabilities.

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.

What You’ll Be Doing

• 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

• 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.

• Design systems that support model evaluation, prompt/version management, and deterministic fallbacks to ensure AI-driven features are observable, testable, and production-safe.

• 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