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.