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Job Description

JPMorganChase invites you to apply for the AI Engineer - Lead Software Engineer role in Jersey City, onsite. The position offers a salary range of USD 152,000 to 215,000 per year. You will lead the architecture and hands-on development of scalable large language model systems and agentic AI platforms for enterprise use, champion cloud-native design on AWS, and establish evaluation and observability standards that elevate reliability, cost efficiency, and developer velocity across teams.

Benefits

  • comprehensive health care coverage
  • on-site health and wellness centers
  • retirement savings plan
  • backup childcare
  • tuition reimbursement
  • mental health support
  • financial coaching

Responsibilities

  • Spearhead the architecture and hands-on delivery of scalable agentic AI platforms that serve enterprise workflows
  • Develop production-grade AI systems featuring agents, capabilities, memory patterns, guardrails, and orchestration of tool usage
  • Design retrieval and context-engineering strategies such as embeddings, semantic search, grounding, summarization, and management of prompts and versions
  • Build cloud-native AI services on AWS using containers, serverless patterns, event-driven messaging, and distributed data stores
  • Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls
  • Create well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes
  • Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior
  • Define engineering standards for reliability, security, and safe AI operation throughout the platform lifecycle
  • Mentor senior engineers and influence engineering direction through code reviews, architecture discussions, and cross-team technical leadership
  • Leverage enterprise-approved AI coding assist tools to enhance code quality and delivery speed, validating outputs via peer review, automated tests, and secure coding practices
  • Share learnings and reusable patterns to boost broader team effectiveness
  • Apply SDLC toolchain knowledge, including enterprise AI-assisted development and automation capabilities, to maximize the value of automation

Requirements

  • Formal training or certification in software engineering with at least five years of practical experience
  • Experience designing and shipping production large language model applications, including agentic workflows and tool integration patterns
  • Strong software engineering fundamentals and proven ability to deliver cloud-native services on AWS using containers and serverless designs
  • Proficiency in designing distributed systems with asynchronous workflows, durable messaging, and scalable data access patterns
  • Experience building retrieval-augmented generation solutions, including embeddings, semantic search, grounding, and prompt lifecycle/versioning
  • Proven ability to implement evaluation and monitoring for model quality, reliability, and safe behavior over time
  • Strong API design skills with secure integration patterns and reusable platform capability development
  • Demonstrated technical leadership, including mentoring, driving architecture decisions, and influencing cross-functional stakeholders
  • Hands-on experience using enterprise-authorized AI-assisted development tools, with the ability to critically evaluate and refine AI-generated outputs for correctness, performance, and security
  • Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe usage within team practices

Technologies

AWS, Kubernetes, LLM Suite

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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