Virtasant logo
Virtasant

We are a global, diverse team of cloud experts building the next generation of cloud solutions.

Senior Backend Engineer – AI Platform

Backend EngineerSoftware EngineerFull TimeRemoteSeniorTeam 51-200H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

82 days ago

Salary

0

Seniority

Senior

Bachelor Degree8 yrs expEnglishAWSJavaPythonSDLC

Job Description

Senior Backend Engineer – AI Platform

Virtasant

• 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 • 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.

Job Requirements

  • 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.

Benefits

  • High ownership
  • Product focus
  • AI-First
  • Opportunity to own and shape technical delivery at scale
  • Exposure to broader cloud optimisation and consulting initiatives over time

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