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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
Software Development Engineers are expert problem-solvers and builders who design, implement, and improve software applications and systems. In this role, engineers spend approximately 60–75% of their time coding, working hands-on with code, data, and modern tools (including AI-assisted development, cloud services, and automation frameworks) to deliver secure, scalable, and high-quality technology solutions that drive business outcomes in the fintech sector. They collaborate with cross-functional teams – product managers, designers, data scientists, QA, operations, and compliance – to translate business requirements into robust technical solutions, all while adhering to best practices, security standards, and regulatory requirements.
All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) to support everyday work.
This role sits within an AI-first engineering team building production-grade agentic systems powered by large language models (LLMs). Engineers design and implement multi-agent orchestration frameworks, retrieval-augmented generation (RAG) pipelines, tool-calling integrations, and LLM inference services — all at enterprise scale on on-premise infrastructure.
Key Responsibilities:
- Collaborate with stakeholders to interpret requirements for project components and incorporate feedback into future designs or solution fixes.
- Translate business requirements by verifying assumptions and escalating potential design issues to appropriate stakeholders.
- Participate in system design and architecture, refining code plans and contributing to design documentation.
- Contribute to project estimation and escalate issues that may cause delays.
- Develop, implement, and maintain code for products, services, or components, applying coding patterns, guidelines, and best practices.
- Apply debugging tools to verify assumptions and proactively flag issues before they occur.
- Participate in code reviews to ensure coding standards are followed and address routine pull requests.
- Create test plans and configure testing procedures to identify and resolve defects for multiple features.
- Respond to support requests during on-call rotations, troubleshooting issues and deploying fixes under guidance.
- Leverage and build knowledge of software developer tools to create, debug, and maintain code for components.
- Remain current in skills by investing time in training resources to improve product availability, reliability, efficiency, observability, and performance.
AI & Agentic Systems
- Design, implement, and tune multi-agent LLM workflows using agent orchestration frameworks (e.g., LangGraph, LangGraph4j, LangChain4j), including declarative graph compilation, conditional routing between agent nodes, state persistence, and dynamic agent spawning at runtime.
Build and maintain ReAct (Reasoning + Acting) agent loops — iterative Reason Act* Observe cycles in which an LLM selects tools, interprets tool results, and continues until a terminal answer is produced.
- Implement Supervisor/Orchestrator agent patterns where a Lead Agent dynamically plans, spawns, and coordinates Specialist Sub-Agents using conditional graph edges and tool frameworks.
- Develop MCP (Model Context Protocol) tool servers that expose enterprise data sources as callable tools for AI agents, enabling natural-language-driven access to structured backend systems.
- Implement and refine RAG (Retrieval-Augmented Generation) pipelines: document ingestion and chunking (PDF/Word), embedding generation, vector storage (e.g., PostgreSQL + pgvector), and semantic similarity retrieval to ground LLM responses in authoritative source documents.
- Integrate with enterprise LLM inference gateways via REST APIs, including request shaping, prompt template management, token budget control, and graceful degradation under service unavailability.
- Design and implement agent memory architectures — both short-term per-agent isolated context stores and long-term shared knowledge repositories used across agent workflows.
- Build confidence scoring and uncertainty quantification mechanisms for AI-generated outputs, including threshold-based routing of low-confidence results to human review.
- Implement agent feedback loops — capturing human reviewer decisions and feeding structured corrections back into prompt templates and knowledge repositories to continuously improve model behavior.
- Write LangChain4j / LangChain tool definitions (e.g., Java @Tool-annotated methods) and wire them into ReAct agents for data transformation, external API calls, and domain-specific processing.
- Build and maintain agentic audit trails — structured logs capturing every tool call, agent decision, LLM prompt/response, and state transition for traceability and explainability.
- Develop and test Spring Boot microservices (Java 21, Spring Boot 3.5.x) that host AI orchestration engines, expose REST APIs for downstream consumers, and integrate with upstream data source systems.
- Author and maintain Helm charts and Jenkins CI/CD pipelines for containerized deployment of AI services to Kubernetes/OpenShift clusters across multi-region on-premise data centers.
- Build end-to-end and integration tests for non-deterministic AI components — including prompt regression suites, tool call mock frameworks, agent output schema validators, and determinism gates to catch silent prompt drift before production.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
- 2 or more years of work experience with a Bachelor’s Degree or an Advanced Degree (e.g. Masters, MBA, JD, MD, or PhD)
Preferred Qualifications:
- 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
- 2+ years of relevant work experience and a Bachelor's degree, OR 5+ years of relevant work experience.
- 3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD).
- Experience in technologies/software systems or a directly related field (minimum two years).
- Experience in developing and/or implementing web-based or mobile applications (minimum two years).
- Experience in system design and architecture for product components.
- Experience in debugging and troubleshooting software issues.
- Experience in code review and applying coding standards.
- Experience in test planning and execution for software features.
- Experience in responding to support requests and deploying fixes.
- Experience in using software developer tools for code creation and maintenance.
- Experience developing backend services in Java (Java 17+ preferred; Java 21 a strong plus).
- Familiarity with REST API design, Spring Boot, and JPA/ORM-based data access patterns.
- Foundational understanding of LLM APIs — prompt construction, token limits, and response parsing.
- Experience building and testing enterprise-scale web services (minimum one year).
- Experience working on client-facing project or technical teams (minimum one year).
- Experience in integrating feedback into design and solution fixes.
- Experience in mentoring junior engineers and collaborating with cross-functional teams.
- Hands-on experience building agentic AI systems using LangGraph, LangChain, LangGraph4j, or LangChain4j — specifically multi-agent graph construction, node/edge definitions, conditional routing, and state schema design.
Experience implementing the React prompting pattern (Thought Action* Observation) in a production or near-production LLM application.
- Working knowledge of Model Context Protocol (MCP) — server registration, tool schema definition (tools/list, tools/call), and client-side integration.
- Experience building RAG pipelines: document chunking strategies, embedding models, vector database querying (pgvector, Pinecone, Weaviate, or equivalent), and retrieval relevance tuning.
- Experience with prompt engineering — including structured output enforcement (JSON schema), chain-of-thought prompting, few-shot example design, and system prompt management.
- Experience building and monitoring LLM observability — tracking token usage, latency per agent step, tool call success rates, and output quality metrics in production.
- Experience with PostgreSQL including the pgvector extension for embedding storage and similarity search.
- Experience deploying containerized workloads on Kubernetes or OpenShift, including Helm chart authoring, rolling deployments, and health probes.
- Experience designing audit logging for AI agent decisions — capturing inputs, reasoning traces, tool calls, and outputs in a structured, queryable format.
- Familiarity with Aspect-Oriented Programming (AOP) for cross-cutting concerns such as agent call logging, latency measurement, and security enforcement in Spring Boot.
- Experience with CI/CD pipelines (Jenkins or equivalent) for AI/ML service deployments, including version gating and environment promotion strategies.
Information for US Applicants
For roles located in the US, the estimated salary range for this position is $110,700.00 to $ 171,800.00 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 $110K-$171K range is below 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. This role's midpoint ($141K) sits 33% below the category median. Disclosed range: $110K to $171K.
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
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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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