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About This Role
About Us
Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.
Job Description
Shape the Future of Enterprise AI at Scale
We are looking for a seasoned Senior Staff Fullstack Engineer (Sr. Consultant title at Visa) to join our Corporate Generative AI Technologies team. In this role, you will help architect, build, and scale enterprise-grade Generative AI and agentic applications. This is a senior, hands-on engineering role for someone who brings strong system design judgment, full-stack product engineering depth, and the ability to translate complex business workflows into scalable, reliable AI-enabled automation solutions.
You will work on platforms and applications that use AI-native application patterns, including large language models, agentic workflows, retrieval-augmented generation, tool orchestration, API integrations, ETL pipelines, and data systems to transform business processes into intelligent, reliable, and scalable workflows. You will help solve hard engineering problems such as decomposing complex workflows into reusable agents, designing secure human-in-the-loop systems, and building production-grade GenAI applications that can be monitored, evaluated, governed, and continuously improved.
As a Senior Consultant (Staff Software Architect), you will operate with a high degree of autonomy, ownership, and technical judgment while collaborating closely with your manager and the broader engineering team to align on technical direction. You will be responsible for building high-quality software while also influencing system design decisions, architectural direction, engineering standards, and best practices across the team.
Key Responsibilities
- Design, build, and scale enterprise-grade GenAI and agentic applications, with a strong focus on maintainable, secure, scalable, reliable, and production-ready architecture.
- Own architecture and delivery of major GenAI subsystems; lead design reviews; mentor I4/I5 engineers; define reusable patterns and production standards.
- Apply strong system design judgment to build full-stack, production-grade applications with robust API design, workflow orchestration, secure data flows, observability, and operational readiness.
- Build modern frontend experiences using React and established frontend patterns, including component-based architecture, state management, reusable UI components, accessibility, performance optimization, and seamless integration with backend APIs and AI-enabled services.
- Design and implement scalable backend services using Python, Node.js, and/or Java, including secure APIs, asynchronous processing, background jobs, authentication, authorization, logging, error handling, and system resiliency.
- Work with databases like PostgreSQL, Redis, vector databases, and related technologies, including schema design, indexing strategies, query optimization, transaction management, caching patterns, migrations, and data access patterns.
- Implement backend capabilities for AI-enabled and agentic workflow automation, including intent routing, agent orchestration, tool execution, API integrations, data retrieval, multi-step execution, workflow state management, human approval flows, guardrails, auditability, and enterprise system integration.
- Develop AI-native capabilities using OpenAI, Anthropic, and related LLM APIs/SDKs, including prompt orchestration, tool/function calling, structured outputs, streaming responses, model routing, and evaluation patterns.
- Apply deep knowledge of modern LLM capabilities to make informed engineering decisions around model selection, context management, latency, cost, reliability, output quality, safety, and user experience.
- Design and implement retrieval-augmented generation solutions, including ingestion pipelines, ETL workflows, embeddings, vector database integration, retrieval strategies, relevance ranking, grounding, and retrieval quality evaluation.
- Build and deploy cloud-native applications using containers, DevOps practices, CI/CD pipelines, automated testing, monitoring, and operational automation.
- Work closely with engineering teammates and cross-functional partners to align with team priorities, translate ambiguous requirements into proof-of-concepts, then evolve them into production-quality solutions through shared ownership and hands-on collaboration.
- Implement observability and operational excellence for GenAI applications, including end-to-end tracing, workflow telemetry, model evaluation, and monitoring to ensure secure, reliable, and production-ready AI systems.
Technical Skills:
- Languages & Frameworks: Python, FastAPI, LangGraph
- Cloud & Infrastructure: AWS, Azure, Docker, Kubernetes, ECS
- DevOps: Git, CI/CD pipelines
- Anthropic / OpenAI SDKs, MCP, A2A
- Databases & Storage: Pinecone, Redis, PostgreSQL
- Monitoring & Governance: Prometheus, Grafana, audit logging, access control
- Frontend: ReactJS, HTML, CSS, JavaScript
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 8+ years of relevant work experience with a Bachelor’s Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11+ years of relevant work experience.
- At least 8 years of experience designing, building, and operating complex distributed software systems in production environments.
- Strong background in software architecture, distributed systems, API design, cloud-native platforms, and data-intensive applications.
- Experience with cloud platforms, containerized environments, CI/CD systems, and observability tooling.
- Hands-on experience with React and backend development using Python, Node.js, Java, or similar languages.
- Strong communication skills with the ability to translate complex technical concepts to business stakeholders
- Experience driving technical strategy and influencing architectural direction across organizations.
- Expertise in system design tradeoffs involving scalability, security, reliability, performance, cost, and developer productivity.
Preferred Qualifications:
- 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
- Experience building AI-enabled, data-driven, workflow automation, search, conversational, or machine learning-powered applications is highly valued.
- Direct experience with Generative AI technologies, LLMs, RAG architectures, or agentic systems is preferred but not required for candidates with exceptional software architecture and distributed systems experience.
- Deep understanding of modern LLM ecosystems, agent frameworks, retrieval architectures, and enterprise AI deployment patterns.
- Familiarity with modern AI architectures including agentic workflows, memory systems, tool orchestration, MCP, and A2A frameworks
- Proven ability to lead cross-functional AI initiatives, including PoC development, stakeholder alignment, and enterprise rollout.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $162,500.00 to $ 260,400.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.Work Hours
Varies upon the needs of the department.
Travel Requirements
This position requires travel 5-10% of the time.
Mental/Physical Requirements
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
Salary Context
This $162K-$260K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Visa, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $162K to $260K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Visa AI Hiring
Visa has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Data Scientist. Positions span San Francisco, CA, US, Foster City, CA, US, Ashburn, VA, US. Compensation range: $171K - $326K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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