FinOps Manager

Description:

PassportCard Labs is where innovation meets impact. As the technological powerhouse behind PassportCard, we design and build cutting-edge solutions that revolutionize the insurance industry. Whether it’s real-time claims payments or predictive analytics, our Labs team is at the forefront of driving change and shaping the future of insurance. Ready to innovate with us?

We are looking for a FinOps Manager to lead cloud and AI cost optimization, improve financial accountability, and maximize the value of our cloud and AI/ML workloads. This role works across Engineering, DevOps, Data, AI/ML, Finance, and Leadership to drive visibility, efficiency, and governance in both cloud infrastructure and AI consumption and will be reporting directly to the VP of IT Infrastructure.  

Responsibilities 

Cloud & AI Cost Optimization 

• Own and evolve the organization’s FinOps and AIOps cost strategy. 
• Monitor and optimize spend for: 
• Cloud compute, storage, networking 
• SaaS AI services (e.g., OpenAI, Vertex AI, Bedrock, Azure OpenAI) 
• Provide actionable recommendations for model selection, right-sizing, and inference optimization. 

Reporting & Governance 

• Build dashboards for real-time visibility into cloud and AI cost drivers. 
• Implement cost allocation models for training vs. inference, environments, and business units. 
• Define tagging standards, budgeting processes, and consumption guardrails. 
• Track unit economics such as cost per inference, cost per model training run, and per-product AI usage. 

Cross-Team Collaboration 

• Partner with engineering, Data/AI teams, and finance to plan and forecast cloud & AI consumption. 
• Guide teams in designing efficient architectures and model deployment strategies. 
• Educate stakeholders on cost-aware engineering and AI usage best practices. 

• Proven experience in FinOps, Cloud Cost Management, AI/ML Infra, or Cloud Architecture. 
• Strong understanding of AWS/Azure/GCP billing and AI pricing models. 
• Experience with AI/ML workloads: training pipelines, GPUs, inference optimization, and model lifecycle management. 
• Experience with cost tools such as AWS Cost Explorer, Kubecost, CloudHealth, Datadog, or AI usage analytics tools. 
• Strong analytical, modeling, and communication skills.