AI Finops Development Engineer

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us challenge the status quo and transform the finance industry together.

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).

As an AI FinOps Development Engineer within Schwab Technology Services, you’ll help shape how AI is adopted and scaled across the enterprise by building capabilities that connect AI consumption with costs, budgets, organizational ownership, and business value.

Working at the intersection of developer experience, platform engineering, and applied AI, you’ll turn complex usage and financial data into scalable solutions that improve visibility into AI investments, support informed decision-making, and advance engineering productivity, quality, and standards. This highly visible, hands-on role is ideal for someone who enjoys solving complex technical challenges, enabling others, and helping shape the future of software delivery.

Using your software development, data analytics, and problem-solving skills, you’ll develop data pipelines, integrations, APIs, dashboards, forecasting, cost-allocation, and governance capabilities across a growing portfolio of AI platforms and models. You’ll collaborate across Finance, engineering, architecture, security, identity and access management, and AI platform teams to deliver reliable, auditable solutions. Your work will strengthen data accuracy, identify spending and utilization opportunities, and help Schwab responsibly scale AI through effective financial management and governance.

• →Delivery of AI-enabled solutions that improve overall product quality, drive consistency in developer practices and significantly increase developer productivity across the organization.
• →Design, evolve, and enhance AI solutions to effectively support a diverse cross-section of STS developers, ensuring usability, scalability, and alignment with enterprise development standards.
• →Evaluate, pilot, and operationalize emerging AI developer tools (e.g., code assistants, AI-powered testing, automated code review) to determine enterprise fit, define rollout strategies, and manage adoption lifecycle.
• →Build and curate reusable prompts, templates, and AI-augmented workflows that lower the barrier to entry and help developers get productive with AI tools quickly.
• →Act as a recognized subject-matter expert and thought leader for AI-assisted development, engaging with developer communities through demos, office hours, brown bags, and internal forums.
• →Partner closely with engineering, architecture, and platform teams to ensure AI capabilities are well-integrated into existing development workflows and toolchains.
• →Create and maintain clear, high-quality technical documentation to support solution adoption, onboarding, and long-term sustainability.
• →Contribute to training programs, enablement materials, and internal communications to drive awareness, understanding, and effective usage of AI solutions.
• →Continuously evaluate feedback from developer communities and stakeholders to refine solutions and maximize business and technical value.
• →Establish ways to measure success and outcomes, and use data to refine standards, improve reliability, and accelerate lead time for change.
• →Continuously evolve the roadmap based on business priorities, team feedback, and changes in cloud, automation, AI-assisted delivery, and developer experience.
• →Responsible for operationalizing AI solutions developed by internal teams, integrating them into the developer environment, and supporting their enterprise-wide deployment and maintenance.

• 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 strong 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 apply analytical thinking and problem solving to 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.
• Demonstrated 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 the ability to explain 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.

• 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.

In addition to the salary range, this role is eligible for bonus or incentive opportunities.

Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.