Lead Engineer

Lead Engineer

| Experience: 8 - 10 Years | Openings: 1 | Location: Bangalore

Job Summary

As a Senior AI Platform FinOps Engineer, you will own the cost and consumption management layer of the AI platform. You will partner across platform engineering, data, security, finance, and product teams to make AI usage measurable, governable, efficient, and aligned to business value. This role covers model and token cost tracking, chargebacks, budget controls, cost observability, environment efficiency, and financial governance for AI platform services.

Responsibilities

• Build cost transparency across AI platform services, including model usage, token consumption, infrastructure spend, storage, retrieval, and inference operations.
• Design and operate usage-based chargeback and showback models for teams, products, and business units consuming AI platform services.
• Define and monitor budgets, s, thresholds, and anomaly detection for model usage, gateway consumption, and platform operating costs.
• Partner with platform teams to optimize cost across model routing, failover strategies, semantic caching, autoscaling, storage tiers, and environment utilization.
• Develop dashboards and reporting that connect AI platform spend to business outcomes, token efficiency, reliability, and adoption metrics.
• Drive cost-aware operating standards for AI experimentation, prompt evaluation, benchmarking, and production deployments.
• Evaluate model provider, infrastructure, and serving patterns for cost-performance tradeoffs, including multi-model routing and lifecycle decisions.
• Partner with engineering teams to create golden paths and guardrails that reduce waste and improve efficiency in model training, deployment, and runtime operations.
• Build governance practices around financial accountability, tagging, ownership, approvals, and operational reviews for AI services.
• Support executive and engineering reporting on AI platform efficiency, spend trends, and value realization.

Required qualifications

• 7+ years of experience in cloud engineering, platform operations, FinOps, infrastructure cost management, or a related technical operations discipline.
• Strong understanding of cloud cost drivers across compute, storage, network, containers, managed services, and high-scale platform environments.
• Experience building cost dashboards, usage reports, budget controls, or chargeback models for shared platforms or engineering organizations.
• Strong analytical skills with the ability to connect operational telemetry to financial insights and business value.
• Experience working with AI or data platform cost patterns, including model serving, experiment environments, storage, and retrieval workloads.
• Ability to work closely with engineering teams on practical optimization opportunities without slowing delivery.
• Strong communication skills and the ability to translate technical usage into actionable financial recommendations.

Preferred qualifications

• Experience with AI cost telemetry such as token usage, prompt efficiency, inference cost, model comparison, and ROI assessment.
• Experience with enterprise AI gateways, model catalogs, or centralized AI platform controls.
• Experience with Databricks, ML platforms, GPU-backed environments, or platform cost governance for advanced analytics and AI workloads.
• Experience partnering with finance, procurement, security, and platform engineering teams in enterprise operating models.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.