Full Job Description
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 senior 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 architect or senior engineer who enjoys working cross-functionally 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 design and ship, the standards you model, and the way you partner with architects, engineers, and stakeholders to solve complex problems with clarity, precision, and accountability. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.
What you have
Required Qualifications
• Bachelor’s or Master’s degree in Computer Science or equivalent professional experience.
• 8+ years of software engineering experience, with demonstrated expertise designing and delivering complex, scalable software solutions and driving technical outcomes across the development lifecycle.
• 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 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.
Preferred Qualifications
• Advanced degrees in Computer Science or Artificial Intelligence.
• 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.
In addition to the salary range, this role is eligible for bonus or incentive opportunities.
