AI Finops Development Engineer

Charles Schwab Inc.•Southlake, TX

1d•$129,000 - $175,000•Onsite

About The Position

As an AI FinOps Development Engineer within Schwab Technology Services, you will build capabilities that connect enterprise AI consumption with costs, budgets, organizational ownership, and business value. Working across developer experience, platform engineering, and applied AI, you will turn usage and financial data into scalable solutions that improve visibility into AI investments, support informed decisions, and advance engineering productivity, quality, and standards. You will develop data pipelines, integrations, APIs, dashboards, forecasting, cost-allocation, and governance capabilities across AI platforms and models. You will collaborate with Finance, engineering, architecture, security, identity and access management, and AI platform teams to deliver reliable, auditable solutions that strengthen data accuracy, identify spending and utilization opportunities, and support responsible enterprise AI adoption. The role also focuses on operationalizing AI-enabled development solutions and integrating them into developer environments and workflows. You will evaluate and adopt AI developer tools, create reusable prompts and workflows, support developer enablement, measure outcomes, and evolve solutions in response to feedback and business priorities. The position is expected to work on site at the specified location(s). The role is eligible for bonus or incentive opportunities in addition to the salary range.

Requirements

At least 7 years of experience in software engineering, data engineering, platform engineering, cloud engineering, FinOps, or a related technical role.
Hands-on experience developing production applications, data pipelines, APIs, or analytics platforms, including integrations with REST APIs, event streams, databases, cloud services, files, or vendor platforms.
Experience with AI platforms and developer tools such as GitHub Copilot, Microsoft Copilot Studio, Azure AI Foundry, OpenAI, Anthropic, Google Vertex AI, or similar services.
Experience supporting token-based, credit-based, seat-based, or hybrid AI pricing and consumption models.
Experience with relational or analytical data stores, including data modeling, transformation, validation, and reconciliation, with a focus on data accuracy and traceability.
Understanding of cloud or SaaS cost management, including budgeting, forecasting, allocation, showback, chargeback, unit economics, or optimization.
Ability to analyze complex financial and usage data and translate findings into actionable dashboards and reporting.
Experience with modern software delivery practices, including source control, CI/CD, automated testing, infrastructure as code, observability, and production support.
Knowledge of enterprise security, privacy, identity and access management, audit, and data governance practices.
Ability to collaborate across engineering, Finance, architecture, security, platform, and business teams and influence outcomes without direct authority.
Strong written and verbal communication skills, including explaining technical, cost, and usage information to technical and nontechnical audiences.
Bachelor’s degree in Computer Science, Engineering, Information Systems, Finance Technology, or a related field, or equivalent practical experience.

Nice To Haves

Experience with FinOps, Technology Business Management, total cost of ownership, SaaS management, or cloud cost-management platforms such as Apptio.
Experience developing forecasting, capacity planning, spend-anomaly detection, license-utilization, or cost-optimization capabilities.
Experience designing role-based dashboards and reporting by organization, cost center, product, platform, model, or user.
Experience integrating enterprise identity and access-management solutions and supporting entitlement or approval workflows.
Experience with Azure, AWS, or GCP in hybrid cloud environments and container technologies such as Kubernetes.
Experience building auditable financial data products in a regulated environment.
Familiarity with machine learning or statistical techniques used for forecasting, anomaly detection, recommendation, or pattern analysis.
Knowledge of AI governance, responsible AI, model-access governance, and human-in-the-loop controls.
FinOps Certified Practitioner or relevant cloud, data engineering, security, or architecture certification.

Responsibilities

Deliver AI-enabled solutions that improve product quality, consistency in developer practices, and developer productivity across the organization.
Design, evolve, and enhance AI solutions for a diverse cross-section of STS developers, ensuring usability, scalability, and alignment with enterprise development standards.
Evaluate, pilot, and operationalize emerging AI developer tools, define rollout strategies, and manage their adoption lifecycle.
Build and curate reusable prompts, templates, and AI-augmented workflows to help developers become productive with AI tools.
Serve as a subject-matter expert and thought leader for AI-assisted development through demos, office hours, brown bags, and internal forums.
Partner with engineering, architecture, and platform teams to integrate AI capabilities into existing development workflows and toolchains.
Create and maintain technical documentation for solution adoption, onboarding, and long-term sustainability.
Contribute to training programs, enablement materials, and internal communications about AI solutions.
Evaluate feedback from developer communities and stakeholders to refine solutions and maximize business and technical value.
Establish measures of success and use data to refine standards, improve reliability, and accelerate lead time for change.
Evolve the roadmap based on business priorities, team feedback, and changes in cloud, automation, AI-assisted delivery, and developer experience.
Operationalize AI solutions developed by internal teams, integrate them into the developer environment, and support enterprise-wide deployment and maintenance.
Develop data pipelines, integrations, APIs, dashboards, forecasting, cost-allocation, and governance capabilities across AI platforms and models.
Improve data accuracy, identify spending and utilization opportunities, and support responsible AI scaling through financial management and governance.