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About This Role
JOB DESCRIPTION
Lead enterprise corporate website development at JPMorganChase, driving innovation and operational excellence across corporate web channels through modern engineering practices and AI\-enabled software delivery.
As a Senior Corporate Website Web Developer (AI\-Enabled) on the Performance Marketing team, you will work a team that operates as an internal agency responsible for managing the design, implementation, testing, content production, data analytics, and health of JPMorganChase.com, JPMorgan.com, and several other corporate sites.
Job Responsibilities
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- Develops and implement components, templates, and workflows in an enterprise web content management platform (AEM highly desired; comparable CMS/WCM platforms acceptable) for high\-traffic, enterprise\-level websites.
- Builds user interfaces using modern JavaScript (ES6\+) and scalable front\-end patterns suitable for enterprise web properties.
- Applies AI to improve delivery outcomes by designing effective prompts and building/testing developer assistants and agentic workflows that accelerate code build, quality checks, and automated testing within Jenkins/GitHub pipelines.
- Works from Figma wireframes and functional requirements to create accessible, responsive web interfaces with HTML, JavaScript, and CSS.
- Integrates web interfaces with back\-end systems and RESTful services in collaboration with technology teams.
- Troubleshoots and resolve cross\-browser and cross\-platform support issues.
- Optimizes websites for speed, scalability, SEO, and performance, using measurable performance and quality targets.
- Manages day\-to\-day website updates, including production releases/updates, asset trafficking, and code changes.
- Participates in application deployment, performance monitoring, and operational issue resolution, incorporating automation, AI\-assisted analysis where appropriate, and standard cybersecurity scanning/remediation with responsible oversight.
- Collaborates with IT/Development teams for requirements gathering and ongoing website maintenance, reliability, and modernization.
- Works closely with business stakeholders to translate requirements into technical designs, delivery plans, and forward\-looking web strategies.
Required qualifications, capabilities, and skills
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- Strong foundation in front\-end web development for enterprise web experiences.
- In\-depth knowledge of HTML5 and CSS3 (required); SASS/Less.
- Extensive experience with JavaScript (ES6\+) (required); jQuery and/or TypeScript
- Experience with AI prompt engineering and building/refining assistants/agents to support software development workflows (e.g., code generation, code review support, test creation, defect triage, and CI quality gates).
- Ability to integrate AI\-assisted workflows into engineering processes and toolchains (e.g., Jenkins/GitHub) with a focus on repeatability, quality, and speed to market.
- Experience with JavaScript frameworks such as React, Angular, or Vue.
- Experience with enterprise WCM/CMS concepts (components, templates, content models, workflows); AEM experience is highly desired.
- Experience creating models and/or services in Java (11\+) or similar (C\#, JavaScript, etc.).
- Proficiency in RESTful APIs and asynchronous request handling (AJAX, partial page updates).
- Experience with Node.js (16\+) and Webpack (4/5\).
- Strong communication, collaboration, and stakeholder\-management skills, with the ability to coach peers on practical AI\-enabled SDLC adoption and disciplined engineering execution.
Preferred qualifications, capabilities, and skills
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- Hands\-on experience delivering production solutions in Adobe Experience Manager (AEM), including component development, templating, workflows, and publishing patterns at enterprise scale.
- Experience building or operationalizing agentic automation for software delivery (e.g., multi\-step assistants that generate tests, propose fixes, and support CI gating) with clear human oversight and auditability.
- Experience integrating quality controls into Jenkins/GitHub workflows, including automated test execution, quality gates, and release validation.
- Experience with automated testing approaches and frameworks (unit, integration, and UI/E2E), with a track record of improving coverage, reducing flakiness, and accelerating release confidence.
- Experience with performance engineering for high\-traffic web properties, including Core Web Vitals\-style optimization, caching strategies, and measurable performance targets.
- Working knowledge of accessibility practices and standards (e.g., WCAG\-aligned implementation) and applying them consistently across components and pages.
- Demonstrated ability to influence delivery outcomes without formal authority, including mentoring and establishing repeatable practices across a mixed skill set team.
ABOUT US
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission\-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on\-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
Our Consumer \& Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most\-used digital solutions – all while ranking first in customer satisfaction.
Marketing \& Communications teams shape the firm's brand and protect and grow the firm's excellent reputation across the world. They deepen relationships with customers through shared passions with a best in class portfolio of partnerships including Madison Square Garden, The Chase Center \& the US Open. Through the use of data and analytics, they create and deliver marketing campaigns or servicing messages through Chase.com, the mobile app, and paid media channels based on what is best for the customer.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At JPMorganChase, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
JPMorganChase AI Hiring
JPMorganChase has 141 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Jersey City, NJ, US, New York, NY, US, Seattle, WA, US. Compensation range: $120K - $450K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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