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About This Role
JOB DESCRIPTION
Join a forward\-thinking team at JPMorganChase and help shape the future of cloud platform engineering. As a Principal Software Engineer, you'll play a critical role in building and optimizing the platforms that power our data and AI initiatives. You'll collaborate with talented engineers and stakeholders to deliver solutions that are secure, reliable, and scalable. This is your opportunity to influence technology strategy, drive operational excellence, and make a tangible impact across the organization. If you're passionate about cloud, automation, and enabling next\-generation AI/ML capabilities, we want to hear from you.
As a Principal Software Engineer at JPMorgan Chase in the Chief Data and Analytics Office's Data Platforms division, you will lead the technical direction for cloud platform engineering. You will work with cross\-functional teams to design, build, and operate innovative cloud platform solutions. You will help us deliver secure, scalable, and reliable platforms that support our AI/ML and data\-driven initiatives. Your expertise will drive performance, efficiency, and a best\-in\-class developer experience across our engineering organization. You will also help advance our AI\-assisted development practices, and efforts on Agentic/AIOps by leveraging modern AI coding tools and agent frameworks to accelerate engineering velocity and platform innovation at scale.
Job responsibilities
- Provide technical leadership and guidance to the cloud engineering team.
- Lead the design and development of secure, scalable, and reliable cloud infrastructure and platform tools.
- Drive adoption of modern DevEx (Developer Experience) practices and evolve CI/CD and developer tooling to improve delivery speed, quality, and consistency.
- Align platform strategy and roadmaps with business priorities; lead cross\-functional initiatives to modernize SDLC practices.
- Evaluate, integrate, and govern strategic tooling to reduce cognitive load and improve developer experience.
- Collaborate with development teams to identify and eliminate bottlenecks on the platform.
- Define and promote paved paths and self\-service workflows to streamline developer workflows.
- Implement real\-time telemetry pipelines and workflows for large\-scale platform observability and analytics.
- Champion adoption of tools that can improve developer productivity through clear documentation, training, office hours, and close engagement with the developer community.
- Standardize use of AI\-assisted coding tools and AI\-powered development ecosystems to accelerate development workflows, code generation, and engineering productivity across the organization.
- Contribute to the design and development of AI agents and Model Context Protocol (MCP) integrations using frameworks built on top of Google ADK, Anthropic SDKs, etc. and related tooling to enable intelligent, scalable platform automation.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10\+ years applied experience
- Hands\-on experience with at least one major cloud provider (AWS, Azure, or GCP).
- Advanced knowledge of containerization and orchestration platforms (Docker, Kubernetes, ECS, etc.).
- Demonstrated expertise in DevEx (Developer Experience) and CI/CD tools (Jenkins, Spinnaker, Bitbucket, GitHub, etc.).
- Strong knowledge of cloud security best practices, shift\-left methodologies, and DevSecOps processes.
- Strong programming skills in Golang or Python, with a solid understanding of software development best practices.
- Proficiency with cloud infrastructure provisioning tools (Terraform, KRO, Crossplane, etc.).
- Experience with logging and monitoring tools (Splunk, Grafana, Datadog, Prometheus, etc.).
- Deep understanding of cloud infrastructure design, architecture, and migration strategies.
- Demonstrated proficiency with AI\-assisted coding workflows, including experience with LLM\-powered development tools, spec\-driven development methodologies, and prompt engineering for software engineering use cases.
Preferred qualifications, capabilities, and skills
- Master's degree in a related field and certifications in Cloud, Kubernetes, or infrastructure\-as\-code technologies.
- Experience implementing multi\-cloud architectures and leading end\-to\-end platform development efforts.
- Background in designing and developing scalable AI/ML or Data platforms.
- Experience with automation and workflow orchestration for operational efficiency.
- Published contributions to open\-source or industry\-recognized projects.
- Hands\-on experience building AI agents and MCP servers/integrations at scale using frameworks such as Google ADK, Anthropic SDKs, and standard agent orchestration tooling.
- Experience with enhancing AI\-powered coding ecosystems with enterprise specific tooling to improve developer productivity and platform engineering workflows.
FEDERAL DEPOSIT INSURANCE ACT:
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
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.
JPMorgan Chase \& Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
Salary Context
This $204K-$285K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($244K) sits 12% above the category median. Disclosed range: $204K to $285K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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