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About This Role
JOB DESCRIPTION
Help shape how teams across the firm build AI agents. You will lead engineering for a flagship, high-visibility AI Agent Platform—the SDK, orchestration frameworks, and reusable components that power agentic AI experiences across the organization. Beyond any single chat experience, you will own the foundational tooling that lets teams compose agents—conversational, voice, and document-extraction—ground them in enterprise data, give them durable agentic memory, and continuously improve accuracy on hard capabilities like NL2SQL and RAG. You will raise delivery standards, turn complex problems into reliable production systems, and set the quality bar for infrastructure that many teams and thousands of users depend on. Join a team where strong engineering, thoughtful collaboration, and continuous learning are core to how we operate.
As Lead Software Engineer – AI Agent Platform at JPMorganChase within Enterprise Technology, AI and Machine Learning & Data Platforms, you will lead the technical design and delivery of the SDK and platform capabilities that enable large-language-model-powered agents at scale. You will own the building blocks agent orchestration, tool/function calling, retrieval and grounding, voice interaction, document extraction, and agentic memory and drive measurable, iterative improvement ("hill-climbing") of capabilities such as NL2SQL and RAG. You will translate high-impact problems into production-grade solutions, from discovery and design through deployment and ongoing operations, while partnering closely with product and stakeholder groups to deliver measurable outcomes.
Job responsibilities
- Lead end-to-end delivery of the AI Agent Platform SDK and frameworks—from problem framing and technical design through production deployment, scaling, and monitoring—that engineering teams use to build, evaluate, and operate AI agents.
- Own core platform services and reusable building blocks: agent orchestration, tool/function calling, retrieval-augmented generation pipelines, and model-serving integration.
- Build and operate first-class agent modalities on the platform, including voice agents (speech-to-text, text-to-speech, low-latency streaming, and turn-taking), document-extraction agents (parsing, OCR, structured field and table extraction from complex documents), and agentic memory (short- and long-term memory, persistence, retrieval, and context management across sessions).
- Drive systematic hill-climbing of core agent capabilities—including NL2SQL, RAG, voice, document extraction, and tool use—by building evaluation datasets, benchmarks, and quality metrics, then iterating on prompts, retrieval, and orchestration to measurably improve accuracy.
- Establish engineering standards through hands-on system design, rigorous code review, and mentorship to improve reliability, maintainability, latency, and developer experience for teams building on the SDK.
- Build and operationalize evaluation, testing, and observability capabilities (offline eval harnesses, regression suites, tracing, latency and cost telemetry, logs, and analytics) to continuously improve solution quality.
- Implement robust safety and governance patterns—guardrails, content filtering, prompt-injection defenses, access controls, and audit-ready operational practices aligned to enterprise expectations.
- Partner with product managers and stakeholders to shape the roadmap, define success metrics (capability accuracy, adoption, task completion, quality), and prioritize work that delivers measurable business impact.
- Drive cross-functional alignment across engineering, data, security, and risk partners to ensure the platform is secure, stable, performant, and scalable.
- Contribute to technical documentation, SDK references, reference implementations, and enablement content that accelerates adoption and responsible usage.
Required qualifications, capabilities and skills
- Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience.
- Advanced proficiency in Python with strong software engineering fundamentals, including testing, design patterns, version control, and code review practices.
- Hands-on experience building, evaluating, and deploying machine learning or large-language-model-enabled systems into production environments—ideally reusable libraries, SDKs, or platform services consumed by other engineering teams.
- Practical experience with prompt engineering and retrieval-augmented generation, including building evaluation methods, benchmarks, and quality measurement to systematically improve capabilities such as NL2SQL and RAG.
- Experience designing and operating reliable services, including incident response readiness, performance tuning, and operational stability for data-intensive systems.
- Demonstrated ability to lead technical decisions and deliver outcomes through ambiguity, balancing speed, risk, and long-term maintainability.
- Strong communication skills with the ability to explain technical trade-offs to both technical and non-technical stakeholders.
Preferred qualifications, capabilities and skills
- Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, Google ADK, or custom orchestration) and evaluation tooling for LLM systems.
- Experience building voice agents (speech recognition, text-to-speech, real-time streaming audio, and conversational turn-taking).
- Experience building document-extraction / intelligent document-processing agents (OCR, layout parsing, structured field and table extraction from complex or unstructured documents).
- Experience implementing agentic memory systems—short- and long-term memory, persistence, and context retrieval across sessions.
- Familiarity with vector databases, embedding pipelines, or graph-based memory approaches used in retrieval-augmented generation solutions.
- Experience with continuous integration and continuous delivery practices and containerization (Docker and Kubernetes) for production deployments.
- Experience with cloud and machine learning platforms (for example, Amazon Web Services, Databricks, or comparable platforms).
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.
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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 $156K-$215K range is above the median for AI Agent Developer roles in our dataset (median: $185K across 15 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 9,653 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $214,400 based on 180 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 13% below the category median. Disclosed range: $156K to $215K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
JPMorganChase AI Hiring
JPMorganChase has 227 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Agent Developer. Positions span Plano, TX, Jersey City, NJ, US, Greater London, England, UK. Compensation range: $115K - $325K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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