Senior Software Engineer - Enterprise Architecture & AI Solutions Engineering

Charles Schwab Inc.•Austin, TX

1d•$155,000 - $195,000•Onsite

About The Position

Schwab is seeking a Senior Software Engineer to join Enterprise Architecture Solutions Engineering within Enterprise Architecture and the Office of the Chief Technology Officer. This hands-on senior engineering role helps build and evolve internal platforms, reusable frameworks, AI tooling, golden paths, agentic infrastructure, and governance guardrails that enable engineering teams to scale AI responsibly and effectively. The engineer contributes to platforms for technical debt management, enterprise governance, agent registries and pipelines, architecture assessment, and internal AI enablement. The role applies engineering judgment to design scalable, secure, observable, production-ready systems, while collaborating across technologies and using AI and large language models to support enterprise engineering needs. Impact comes through systems designed and shipped, modeled standards, and partnerships with architects, engineers, and stakeholders to solve complex problems. The selected candidate is expected to work on site in the specified location(s). Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship. The role is eligible for bonus or incentive opportunities in addition to the salary range.

Requirements

Bachelor’s or Master’s degree in Computer Science, or equivalent professional experience.
At least 8 years of software engineering experience, including expertise designing and delivering complex, scalable software solutions and driving technical outcomes across the development lifecycle.
At least 3 years demonstrating senior-level engineering depth through hands-on design and ownership of full-stack systems shipped end to end.
Strong command of enterprise design patterns, domain-driven design, distributed systems, service interfaces, and production-ready architecture.
Ability to decompose complex problems, define precise technical specifications, direct AI-assisted development effectively, and critically evaluate model-generated output.
Proven experience designing and delivering AI systems in production environments.
Depth in at least two of the following: agentic systems, tool orchestration, MCP or related protocols, retrieval-augmented generation, embeddings, vector search, knowledge retrieval pipelines, prompt/context engineering, formal AI evaluation, testing, and guardrails.
Strong working knowledge of cloud platforms and ability to reason about scalability, resilience, cost, security, and production operations for AI workloads.
Hands-on database expertise across relational, NoSQL, vector, or graph databases, including data modeling, query tuning, indexing, query plans, and storage trade-off decisions.
Experience reviewing code, pairing with engineers, improving development practices, and setting engineering standards through shipped reference implementations.
Strong communication, problem-solving, and collaboration skills, with ability to partner across business, technology, architecture, and engineering audiences.
Commitment to responsible AI practices, including security, privacy, evaluation, governance, and safety built into delivery.
Currently authorized to work in the United States on a full-time basis without employer sponsorship.

Nice To Haves

Advanced degree in Computer Science or Artificial Intelligence.
Experience in private-sector, startup, or highly ambiguous environments, with ownership of problems across multiple technical layers.
Experience mentoring and growing engineers at multiple levels through code review, pairing, technical coaching, and example-setting.
Deep understanding of the software development lifecycle, with examples of improving delivery practices, engineering standards, or team effectiveness.
Experience building reusable engineering frameworks, internal developer platforms, AI enablement tools, or enterprise-scale technical platforms.
Familiarity with coding agents, large language model APIs, orchestration frameworks, retrieval pipelines, model evaluation approaches, and AI governance patterns.
Ability to adapt quickly as priorities, platforms, frameworks, and product needs shift.
Curiosity and a continuous learning mindset, with the ability to stay current as AI engineering practices evolve.

Responsibilities

Build and evolve internal platforms, reusable frameworks, AI tooling, golden paths, agentic infrastructure, and governance guardrails.
Contribute to technical debt management, enterprise governance, agent registries, agent pipelines, architecture assessment, and internal AI enablement solutions.
Design and deliver scalable, secure, observable, production-ready systems.
Design and own full-stack systems end to end, and drive technical outcomes across the development lifecycle.
Decompose complex problems, define technical specifications, direct AI-assisted development, and critically evaluate model-generated output.
Design and deliver AI systems in production environments, incorporating responsible AI practices such as security, privacy, evaluation, governance, and safety.
Review code, pair with engineers, improve development practices, and establish engineering standards through shipped reference implementations.
Partner with architects, engineers, stakeholders, and business and technology audiences to solve complex problems.
Adapt across technologies, platforms, frameworks, and product priorities as enterprise needs evolve.