JP Morgan Chase

Runs a consumer bank and an investment bank

AI/ML Senior Lead Software Engineer - Agentic Pricing

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeJersey City, NJ, USA$175.8k - $260kIn-person

About the Job

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience, including at least 4 years working on AI/ML solutions.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Advanced proficiency in one or more programming languages, with strong Python skills and deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics.
Hands-on experience building production LLM-powered or agentic applications, including tracing, evaluations, and guardrails.
Deep proficiency with Kubernetes and Amazon EKS, micro-VM isolation such as Firecracker, Kata Containers, or gVisor, and sidecar architectures; proven experience designing defense in depth across networking and mutual TLS, workload identity, fine-grained authorization, runtime isolation for untrusted or adversarial workloads, and application-level guardrails.
Practical retrieval-augmented generation experience, including retrieval quality, embeddings, and vector stores; expert knowledge of at least one of AWS, Azure, or Kubernetes.
Knowledge of data management and data model design, and real-time processing using SQL such as Postgres and NoSQL stores such as OpenSearch or Redis.
Proficient in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies such as continuous integration and continuous delivery, application resiliency, and security.
In-depth knowledge of the financial services industry and its IT systems.

Responsibilities

Provide technical guidance and direction to support the business and its technical teams, contractors, and vendors.
Design and ship production agents on NEO across the federated portfolio, owning them from prototype through production.
Build production-grade Graph RAG combining knowledge-graph traversal with vector search, including chunking, ranking, and grounding strategies that keep answers accurate and auditable.
Design agent memory using episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case.
Own organizational context management by assembling entitlement-, lineage-, and tenant-aware context so each agent reasons only over authorized information.
Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers, including Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, and Splunk.
Build task-level and end-to-end agent evaluations, regression suites, LLM-as-judge assessments, and quality safety gating before release.
Deploy and operate solutions on public cloud, including AWS and/or Azure, with strong SDLC, security, resiliency, and observability practices.
Partner with product and business partners across CIB and Payments to turn use cases into shipped, supported agents.
Serve as a function-wide subject matter expert in one or more areas of focus.
Contribute to the engineering community by advocating firmwide frameworks, tools, and Software Development Life Cycle practices.

Desired Qualifications

A Master of Science in Computer Science, Statistics, Mathematics, Machine Learning, or a related field, or equivalent experience.
Experience with agent frameworks or runtimes, A2A, or MCP; agent memory design involving memory nodes and episodic or semantic memory; and organizational context management.
Knowledge of knowledge graphs and graph databases used for retrieval; Graph RAG is a strong plus.
Understanding of LLM fine-tuning and small language model inference.
Ability to develop full-stack products using modern JavaScript/TypeScript frameworks such as Next.js or Svelte for agent user interfaces, including AG-UI and NEO UI SDK.
Experience working in the financial or payments domain at a large institution, such as Investment Banking, Markets, Securities Services, or an adjacent area.
Knowledge of high-performance languages such as Go or Rust.

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Commercial and Investment bank Digital & Platform Services, Data Analytics, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
Design and ship production agents on NEO across the federated portfolio, owning them from prototype through production.
Build retrieval that holds up in production: Graph RAG combining knowledge-graph traversal with vector search, plus chunking, ranking, and grounding strategies that keep answers accurate and auditable.
Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case.
Own organizational context management — assembling entitlement-, lineage-, and tenant-aware context so each agent reasons over only what it’s allowed to see.
Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers (Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, Splunk).
Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality safety gating before release.
Deploy and operate solutions on public cloud (AWS and, or Azure) with strong SDLC, security, resiliency, and observability practices.
Partner with product and business partners across CIB and Payments to turn use cases into shipped, supported agents.
Serves as a function-wide subject matter expert in one or more areas of focus
Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years applied experience with at least 4 years working on AI/ML solutions.
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language(s) - Strong programming skills in Python, with deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics.
Hands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails.
Deep proficiency with Kubernetes and Amazon EKS, micro-VM isolation (e.g., Firecracker, Kata Containers, gVisor), and sidecar architectures, with proven experience designing defense in depth across the stack: network and mTLS, workload identity, fine-grained authorization, runtime isolation for untrusted or adversarial workloads, and application-level guardrails
Practical RAG experience — retrieval quality, embeddings, and vector stores; Expert knowledge of at least one of: AWS, Azure, Kubernetes.
Knowledge of data management and data model design; real-time processing using
both SQL (e.g., Postgres) and NoSQL stores (e.g., OpenSearch, Redis).
Excellent communication skills with the ability to partner effectively with senior technical and business stakeholders.
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems
Preferred qualifications, capabilities, and skills
MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).
Experience with agent frameworks or runtimes, A2A, or MCP. Agent memory design (memory nodes, episodic/semantic memory) and organizational context management.
Knowledge graphs and graph databases used for retrieval. Graph RAG a strong plus.
Understanding of LLM fine-tuning and small language model inference.
Ability to develop full-stack products using modern JavaScript/TypeScript frameworks (e.g., Next.js, Svelte) for agent UIs (AG-UI , NEO UI SDK).
Experience working in the financial or payments domain at a large institution (Investment Banking, Markets, Securities Services, or adjacent).
Knowledge of high-performance languages such as Go or Rust

Base Pay/Salary: Jersey City,NJ $175,750.00-$260,000.00

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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