Senior LLM Cost Optimization Engineer (AI FinOps)

Data Capital Incorporation; McLean (VA); On-site; USD 160,000 - 230,000; Full time

Job summary

Data Capital Inc. is seeking a Senior LLM Cost Optimization Engineer to drive enterprise cost reductions for GenAI infrastructure and inference, without compromising quality.

You will lead hands-on optimization across token usage, model selection, and caching, with strong emphasis on observable metrics and cost controls. Ideal candidates demonstrate 7+ years in software/cloud/FinOps, 2+ years with LLMs, and hands-on AWS Bedrock experience.

Qualifications

• 7+ years in software, cloud/platform engineering, or FinOps, including 2+ years 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 observability/gateway tools (Langfuse, LangSmith, Helicone or similar).
• Strong AWS experience, including Bedrock, Cost Explorer, and cloud cost management.
• Experience building cost dashboards with Grafana/Power BI/QuickSight.
• Must demonstrate measurable production LLM cost savings without compromising quality or performance.

Responsibilities

• Analyze and optimize enterprise LLM API usage, token consumption, and inference costs.
• Implement intelligent model routing, prompt caching, and semantic caching.
• 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.
• Evaluate and optimize AI licensing to eliminate unnecessary platform spending.

Skills

Python

SQL

Automation

Cloud cost analytics

LLM cost optimization

Production cost savings

Tools

Langfuse

LangSmith

Helicone

AWS Bedrock

Grafana

Power BI

QuickSight

Job description

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.