Senior Software Engineer - Enterprise Architecture & AI Solutions Engineering

Charles Schwab is a financial services company seeking a Senior Software Engineer for its Enterprise Architecture Solutions Engineering team. The role focuses on building internal platforms, reusable frameworks, AI tooling, agentic infrastructure, and governance guardrails, while designing scalable, secure, observable, and production-ready systems. The engineer will partner with architects, engineers, and stakeholders to deliver enterprise AI and software solutions.

Responsibilities:

• 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
• 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