Senior Software Engineer - Enterprise Architecture & AI Solutions Engineering
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 (CTO). In this hands-on engineering role, you’ll help build and evolve the internal platforms, reusable frameworks, AI tooling, golden paths, agentic infrastructure, and governance guardrails that enable Schwab engineering teams to scale AI responsibly and effectively. You’ll contribute directly to platforms including technical debt management, enterprise governance, agent registries, agent pipelines, architecture assessment, and internal AI enablement solutions, using strong engineering judgment to design systems that are scalable, secure, observable, and production-ready. This role is ideal for an engineer who enjoys working across technologies, applying AI and large language models as force multipliers, and moving between platforms, frameworks, and product priorities as enterprise needs evolve. Your impact will come through the systems you ship, the standards you model, and the way you partner with architects, engineers, and stakeholders to solve complex problems with clarity, precision, and accountability.
Requirements
- Bachelor's or Master's degree in Computer Science or equivalent professional experience.
- Demonstrated senior-level engineering depth through hands-on ownership of full-stack systems shipped end to end.
- 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 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.
- Strong working knowledge of cloud platforms with the 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 the 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.
Nice To Haves
- Experience working in private-sector, startup, or highly ambiguous environments where you owned 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 continuous learning mindset, with the ability to stay current as AI engineering practices evolve quickly.
Responsibilities
- Help build and evolve internal platforms, reusable frameworks, AI tooling, golden paths, agentic infrastructure, and governance guardrails.
- Contribute directly to platforms including technical debt management, enterprise governance, agent registries, agent pipelines, architecture assessment, and internal AI enablement solutions.
- Design systems that are scalable, secure, observable, and production-ready.
- Apply AI and large language models as force multipliers.
- Move between platforms, frameworks, and product priorities as enterprise needs evolve.
- Ship systems, model standards, and partner with architects, engineers, and stakeholders to solve complex problems.
- Review code, pair with engineers, improve development practices, and set engineering standards through shipped reference implementations.
- Commit to responsible AI practices, including security, privacy, evaluation, governance, and safety built into delivery.
Benefits
- Bonus or incentive opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Senior
