Chief SW Engineer

Visa•Foster City, CA

6d•$230,500 - $369,400•Hybrid

About The Position

The position is responsible for designing, building, and operationalizing production AI systems across the enterprise. This is a hands-on engineering role: you set architecture and engineering standards for agentic AI, generative applications, and ML platforms; you ship reference systems yourself; and you raise the quality bar for every team that builds on AI. This role will be responsible for building the engineering system — platforms, patterns, evals, safety controls, reliability, cost, and developer experience — so business domains can adopt AI at speed with highest attention to security, compliance, and production discipline.

Requirements

12+ years of relevant work experience with a Bachelor’s Degree or at least 9 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 6 years of work experience with a PhD, OR 15+ years of relevant work experience.
Demonstrated track record shipping AI or ML systems that run in production at enterprise scale — not demos or isolated POCs.
Deep fluency with modern generative AI stacks: foundation model APIs, RAG, vector search, tool-use / function calling, agent orchestration, and evaluation frameworks.
Expert-level software craft: Python required; strong additional experience in at least one of TypeScript/Java/Go; rigorous testing, observability, and API design.
Experience designing platforms used by other engineering teams (internal developer platforms, ML platforms, or equivalent).
Strong systems thinking: latency, reliability, cost, data lineage, identity/auth, secrets, and failure modes of probabilistic systems.
Proven ability to operate in a regulated industry (financial services, payments, healthcare, or similarly constrained environments).
Excellent written and verbal communication with executives and with engineers; can write an architecture decision record and a board-ready risk brief.
Bachelor’s in Computer Science or related field required; Master’s or equivalent depth preferred.
Experience in payments, commerce, fraud/risk, identity, or large-scale transaction systems.
Hands-on work with agent frameworks (e.g., LangGraph or equivalent), MCP-style tool protocols, and multi-agent patterns.
MLOps / LLMOps platform experience (feature stores, model registries, experiment tracking, online/offline eval pipelines).
Prior ownership of AI governance engineering: policy-as-code, access minimization, PII handling, model cards, audit trails.
Experience standing up forward-deployed or tiger-team models that embed with business domains.
Open-source contributions, internal platform adoption metrics, or published technical writing that other engineers actually use.

Nice To Haves

15 or more years of experience with a Bachelor’s Degree or 12 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, or MD), PhD with 9+ years of experience.

Responsibilities

Enterprise AI software architecture: reference designs for LLM applications, multi-agent workflows, RAG/grounding, tool-use, and hybrid classical ML + GenAI systems.
Production-grade AI platforms: shared services for model access, retrieval, evaluation, observability, prompt/version management, feature/store integration, and deployment.
Agentic systems at scale: orchestration, memory, tool calling, human-in-the-loop controls, and safe autonomous workflows across business domains.
Quality and safety system: evaluation harnesses, red-teaming, bias/privacy checks, policy enforcement, rollback, canary, and incident response for AI services.
Developer experience for AI: SDKs, templates, CI/CD, golden paths, and inner-loop tooling so domain engineers can ship AI features without reinventing the stack.
Technical strategy and standards: model selection, cost/latency tradeoffs, data contracts, API design, and architecture review for high-risk AI systems.
Cross-domain enablement: partner with product, risk, compliance, legal, security, and domain engineering teams to take use cases from prototype to regulated production.
Define the technical roadmap for AI software platforms and agentic capabilities, aligned to business outcomes.
Architect, lead the design and hands on build flagship agentic systems.
Establish LLMOps / AIOps practices: evals as tests, tracing, cost telemetry, drift detection, model and prompt versioning, and SLOs for AI services.
Set coding, testing, and review standards for AI-adjacent software — Python and TypeScript services, APIs, data pipelines, and infrastructure as code.
Build and mentor a high-leverage bench of staff/principal engineers and AI tiger-team leads; operate as player-coach on the hardest problems.
Partner with security, privacy, risk, and legal to implement responsible AI controls that work in a regulated payments/fintech environment.
Drive build-vs-buy decisions across foundation models, vector stores, orchestration frameworks, and evaluation tooling.
Represent engineering in executive forums: translate technical risk, readiness, and investment into decisions leadership can act on.
Stay current with frontier models and research, but filter aggressively for production fitness, vendor lock-in, and total cost of ownership.