6 days ago
Base Salary
$231k - $369k/yr
Responsibilities
- Define the technical roadmap and architecture for enterprise AI platforms and agentic capabilities.
- Design and hands-on build flagship LLM, RAG, multi-agent, and hybrid ML/GenAI systems.
- Build shared services for model access, retrieval, evaluation, observability, prompt and model versioning, deployment, and developer experience.
- Establish LLMOps/AIOps practices including evaluation, tracing, cost telemetry, drift detection, versioning, and service-level objectives.
- Set software engineering, testing, API, data pipeline, and infrastructure-as-code standards for AI systems.
- Build and mentor staff/principal engineers and AI tiger-team leads while serving as a player-coach on complex problems.
- Implement responsible AI, security, privacy, governance, audit, and compliance controls with cross-functional partners.
- Drive build-versus-buy decisions across foundation models, vector stores, orchestration frameworks, and evaluation tooling.
- Represent engineering in executive forums and communicate technical risk, readiness, and investment decisions.
- Evaluate frontier models and research for production fitness, vendor lock-in, and total cost of ownership.
Requirements
- At least 12 years of relevant experience with a bachelor’s degree, 9 years with an advanced degree, 6 years with a PhD, or 15 years of relevant experience without the degree requirement.
- Bachelor’s degree in Computer Science or a related field, or the applicable advanced-degree or experience alternative.
- Demonstrated experience shipping AI or ML systems into production at enterprise scale.
- Expert Python skills and strong additional experience with at least one of TypeScript, Java, or Go.
- Experience designing platforms used by other engineering teams, such as internal developer or ML platforms.
- Deep knowledge of foundation model APIs, RAG, vector search, tool use, agent orchestration, and evaluation frameworks.
- Strong understanding of latency, reliability, cost, data lineage, identity, authentication, secrets, and probabilistic-system failure modes.
- Experience operating in financial services, payments, healthcare, or another regulated industry.
- Experience with agent frameworks such as LangGraph, MCP-style tool protocols, and multi-agent patterns.
- Experience with MLOps/LLMOps platforms, feature stores, model registries, experiment tracking, and evaluation pipelines.
- Experience implementing AI governance engineering, including policy-as-code, access minimization, PII handling, model cards, and audit trails.
- Experience with payments, commerce, fraud/risk, identity, or large-scale transaction systems is preferred.
- Strong executive and engineering communication skills, including architecture decision records and board-ready risk briefs.
- Open-source contributions, platform adoption metrics, published technical writing, or forward-deployed/tiger-team experience are preferred.
Benefits
- Medical, dental, vision, 401(k), FSA/HSA, life insurance, paid time off, and wellness program.
- At least three days per week in the office; the exact days will be confirmed by the hiring manager.
- The position requires 5–10% travel.
- Work hours vary according to department needs.
- The position is performed in an office setting.
Tech Stack
Categories
About Visa
Visa builds and operates a global digital payments network that connects consumers, merchants, financial institutions, and governments. It earns fees for processing card transactions and offering risk, data, and value‑added services through VisaNet and products like tokenization and tap‑to‑pay. Founded in 1958 and headquartered in Foster City, California, Visa is a public company (NYSE: V) serving clients in more than 200 countries and territories.