Senior LLM Cost Optimization Engineer (AI FinOps)

Data Capital Inc. is seeking a Senior LLM Cost Optimization Engineer with proven experience reducing enterprise Generative AI infrastructure and inference costs while maintaining application quality and performance.

We need a hands-on AI cost optimization specialist—not a traditional cloud FinOps analyst or reporting-only professional.

Key Responsibilities

• Analyze and optimize enterprise LLM API usage, token consumption, and inference costs.

• Implement intelligent model routing, prompt caching, semantic caching, and token optimization.

• Develop AI cost monitoring dashboards, usage analytics, budget controls, and chargeback/showback.

• Optimize AWS Bedrock and other enterprise LLM platforms for cost efficiency.

• Evaluate API-based vs. self-hosted LLM deployment costs and infrastructure utilization.

• Optimize enterprise AI licensing and eliminate unnecessary AI platform spending.

Mandatory Technical Requirements

• 7+ years in software, cloud/platform engineering, or FinOps, including 2+ years working with LLM platforms.

• Strong Python, SQL, cloud cost analytics, and automation skills.

• Hands-on experience optimizing LLM token usage, model selection, caching, and inference costs.

• Experience with LiteLLM, Langfuse, LangSmith, Helicone, or similar LLM observability/gateway tools.

• Strong AWS experience, including Bedrock, Cost Explorer, and cloud cost management.

• Experience building cost dashboards using Grafana, Power BI, QuickSight, or equivalent.

• Must demonstrate measurable production LLM cost savings without compromising quality or performance.

Preferred: AI FinOps, provisioned throughput optimization, GPU infrastructure cost analysis, financial services experience.

Screening Requirement: Applicants must provide one real-world LLM cost optimization project, including the cost reduction achieved, technologies used, and how quality was maintained.