Virtasant
Information Technology & Services
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Location: Remote
Type: Full-time
Team: Cost Optimisation (CO) – Product Engineering
Reports to: Product Lead (CO)
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.
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.
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 scale AI features safely and repeatedly.
8+ years professional software engineering experience.
Strong backend engineering background (full-stack a plus, not required).
Hands-on experience building on AWS.
Strong proficiency in Python (Java/C++ acceptable as secondary languages).
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
Not a Solutions Architect role.
Not client-facing delivery work.
Not a support or “overflow” engineering position.
This role is protected, by design, to focus on product and platform.
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.
After you apply, unlock the direct contact details of the people who actually make the call. A quick follow-up makes you 5x more likely to land an interview.
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