FinOps Architect/Manager in New York, New York

Senior FinOps Consultant AWS & AI Cost Optimization (Architect (Level: Manager)

Interview Mode: Webcam

Duration: Long Term

Location: New York, New York, US

Job Description:

Key Responsibilities

Traditional Cloud & Container Cost Allocation

Architectural Efficiency

FinOps for AI & GenAI Cost Control & Inference & AI for FinOps Operations

Intelligent Automation

• Leverage GenAI-driven FinOps capabilities and tools to automate cloud metadata scanning, anomaly detection, and virtual tagging (VTags).

Non-Deterministic Forecasting

• Utilize machine learning models to transition clients away from static cloud budgeting toward rolling, algorithmic forecasts capable of predicting volatile, agentic AI workloads.

Technical and Professional Expertise

Required Experience & Skills

• 3+ years of direct experience in FinOps, Cloud Cost Management, and Cost Optimization, with a heavy emphasis on the AWS ecosystem.
• 1+ years of practical experience managing AI/ML infrastructure costs or governing LLM/GenAI token pipelines.
• 7+ years of overall experience in cloud computing, infrastructure engineering, or IT financial management.
• Deep Technical Vocabulary: Clear understanding of AI infrastructure primitives, including GPU right-sizing, vector database scaling, context window economics, and prompt caching strategies.
• Financial Literacy: Ability to converse fluently with corporate CFOs regarding CapEx/OpEx shifts, TCO/ROI scenario analysis, and shifting metrics from "total token burn" to "value delivered per token."
• Framework Mastery: Familiarity with the FinOps Framework (Inform, Optimize, Operate) and the newly emerging open standards from the Tokenomics Foundation and FOCUS (Framework for Open Cost Code).

Certifications (Required)

• FinOps Certified Practitioner (FCP) or FinOps Certified Professional.
• AWS Certification (e.g., AWS Certified Solutions Architect Associate or SysOps Administrator).

Desirables

• FinOps Foundation AI Value or Technology Value Certifications.
• Strong background in building data visualization pipelines.
• Academic background in Business, Finance, Data Science, or Computer Science.