Senior Applied AI Engineer

• Design, build, and improve AI-powered capabilities that help customers understand, optimize, and control cloud, SaaS, and AI spend.
• Develop anomaly detection systems that identify unusual spending patterns, waste, and operational risks.
• Build forecasting models for consumption, budgets, cost trends, and capacity planning.
• Design recommendation and optimization engines that identify savings opportunities and rank actions based on expected business impact.
• Develop AI and ML models that improve recommendation relevance, prioritization, explainability, and customer trust.
• Analyze large-scale cost, usage, operational, and telemetry datasets to uncover trends and optimization opportunities.
• Design and implement feature engineering pipelines, experimentation frameworks, and analytical datasets.
• Fine-tune AI systems and agent behaviors to improve quality, consistency, explainability, and reliability.
• Build evaluation frameworks that measure model performance, recommendation quality, forecast accuracy, agent effectiveness, and business outcomes.
• Analyze and eliminate failure modes such as hallucinations, reasoning failures, excessive tool invocation, and agent loops.

Secondary Responsibilities

• Contribute to the design and implementation of autonomous and semi-autonomous FinOps agents.
• Build integrations with MCP servers, APIs, enterprise systems, and optimization workflows.
• Participate in multi-agent system design where specialized agents collaborate to solve customer problems.
• Contribute to the evolution of Mavvrik’s semantic layer, business entities, metadata models, and optimization concepts.
• Mentor engineers through code reviews, design reviews, experimentation practices, and technical guidance.
  • Promote best practices around AI reliability, observability, governance, testing, and production readiness.