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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
The AI Platform Product team is building a scalable, enterprise‑grade platforms that enables teams across the organization to develop, deploy, operate, and govern AI/ML solutions at global scale. The team focuses on providing reliable, secure, and reusable platform capabilities that support the full AI/ML lifecycle \- from data and feature enablement to model training, deployment, monitoring, and optimization.
The team partners closely with engineering, data science, product, and business teams to ensure AI capabilities can be delivered efficiently and responsibly across multiple use cases, including fraud, risk, authorization, and ecosystem protection.
We are seeking a hands‑on Product Analyst with experience across AI/ML platforms, data‑driven product development, and GenAI prototyping. This role is ideal for someone who is technically grounded, product‑oriented, and adaptable, and can work across multiple problem spaces as priorities evolve.
Key Responsibilities:
- Drive and support product requirements for AIP platform capabilities, working hands‑on with engineering and data science teams.
- Contribute to roadmap execution across data platforms, AI/ML lifecycle, GenAI enablement, tooling, and observability.
- Translate business, data science, and platform needs into clear user stories, requirements, and acceptance criteria.
- Lead the product requirements for platform capabilities, working hands‑on with engineering and data science teams. Translate business, data science, and platform requirements into clear Features, user stories, and acceptance criteria.
- Use data and platform metrics to inform product decisions, identify gaps, and support continuous improvement.
- Collaborate effectively across product, engineering, data science, MLOps, infrastructure, and governance teams in an agile environment for faster time to market
- Conduct product acceptance evaluations before launching it to the user teams and support adoption of capabilities.
- Apply hands‑on coding skills to design and build AI/ML and Generative AI prototypes that can be evolved and deployed on scalable platform architectures.
- Collaborate effectively across product, engineering, data science, MLOps, infrastructure, and governance teams in an agile environment.
- Remain flexible and adaptable, supporting multiple product areas and priorities as the platform evolves.
- Understand data processing concepts at scale across on‑prem and cloud environments and apply this knowledge when defining requirements and trade‑offs.
- Own end‑to‑end delivery of assigned platform initiatives from requirements through release and post‑launch validation.
- Contribute to documentation (requirements, release notes, FAQs, run books) to support stakeholders and enable smooth adoption of the AI platform.
This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.
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 hands‑on experience in product management or technical product roles, ideally focused on AI/ML platforms or data‑driven products.
- Strong exposure to AI/ML and Generative AI, with experience across:
- AI platforms, feature platforms and model workflows
- Large Language Models (LLMs), evaluations, and agentic systems
- Experienced on AIML platform on leading public cloud, AWS, Azure, GCP,Databricks
- Hands‑on experience with GenAI prototyping, including use of APIs and basic coding (e.g., Python) to build proofs of concept or demonstrations
- Experience with applied GenAI (e.g., OpenAI, Anthropic) for prototypes and production.
- Solid understanding of data concepts and data processing at scale, across cloud and on‑prem environments.
- Experience launching or supporting products in a highly technical, matrixed organization.
- Exposure to the payments industry or applied AI/ML use cases (fraud, risk, decisioning) or risk technology platform is a strong plus.
- Strong problem‑solving, communication, and collaboration skills.
- Proficiency with software development methodologies such as Agile and experience working with Scrum teams and matrixed organizations.
U.S. Applicants Only
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 MLOps Engineer roles in our dataset (median: $177K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Visa, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $220,000 based on 47 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($141K) sits 36% below the category median. Disclosed range: $110K to $171K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Visa AI Hiring
Visa has 15 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, MLOps Engineer, Data Engineer. Positions span Foster City, CA, US, Austin, TX, US, Highlands Ranch, CO, US. Compensation range: $163K - $451K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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