Lead Software Engineer - Applied AI ML Lead

🏢 JP Morgan
📍 Palo Alto, United StatesFull-timeOn-site
📅 Posted: 1mo ago🔄 Updated: 1mo ago
CV%
✨ AI Summary
JPMorgan Chase is seeking a Lead Software Engineer for their Enterprise Technology, Infrastructure Platforms team. This role involves executing creative software solutions, driving team adoption of AI-assisted engineering practices, and optimizing infrastructure capacity. The engineer will design and develop GenAI/agentic AI solutions, build backend services in Python/Java, and engineer production-grade data pipelines. Responsibilities also include building prompt engineering assets, applying MLOps best practices, and owning the end-to-end AI/ML optimization strategy. The position requires leading evaluation sessions with external vendors and internal teams.
Required Skills
Information Technology
Software EngineeringSystem DesignTestNGSSISData Engineering & AnalyticsApache SparkApache AirflowRAGData ExtractionCopilotDistributed SystemsREST APIMicroservicesCI/CDObservabilityContainerizationKubernetesCloud SecurityReliability EngineeringPythonGenerative AILarge Language ModelsPrompt EngineeringMLOpsData Pipelines
Other
operational stabilityprogramming languageresponsible AIIcebergAI/ML deliveryGenAI deliverysummarizationrankingclassificationproduction engineeringSLMbackend servicesdata ingestion
Finance, Legal & Governance
Valuation
Nice to have:
Information Technology
Model Context ProtocolCopilotClaudeJava
Other
Agent Skillsstructured agentic architecturesVSICloud Foundry
Engineering, Construction & Trades
Packaging
Business, Sales & Management
HR ManagementPerformance Management
🎁 Benefits & Perks
Comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching.
Requirements
Requires formal training or certification in software engineering with 5+ years of experience in system design, application development, testing, and operational stability. Must be proficient in at least one programming language and demonstrate experience with AI-assisted development tools and responsible AI practices. Experience with data engineering stacks (Spark, Airflow, Iceberg), AI/ML/GenAI delivery, and distributed systems (microservices, CI/CD, observability, containers, Kubernetes, security, reliability) is essential. Proficiency in Python is required, with Java being a plus.
Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Enterprise Technology, Infrastructure Platforms team, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 
 

Job Responsibilities
 
  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problem. 
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
  • Own infrastructure capacity optimization solutions and build predictive/prescriptive models to identify capacity risk, performance bottlenecks, and right-sizing opportunities.

  • Design, develop, and productionize GenAI/agentic AI solutions for automation, decision support, and operational workflows, including LLM/SLM apps such as RAG and summarization/extraction.

  • Engineer production-grade backend services in Python/Java (REST APIs, microservices, reusable libraries) and own cloud-native data ingestion/processing pipelines for capacity analytics and AI use cases.

  • Build prompt engineering assets, routing strategies, and guardrails, and implement automated plus human-in-the-loop evaluation to improve quality.

  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  • Apply MLOps best practices across experimentation, versioning, CI/CD, deployment, monitoring, and lifecycle management; implement testing/benchmarking and observability; define success metrics/governance with stakeholders; and mentor engineers to uphold high standards.

  • Own and govern the end-to-end AI/ML optimization strategy—from architecture and engineering standards (quality, lifecycle, observability, secure SDLC) through cross-functional execution with SRE/platform/business—to deliver scalable automation, risk reduction, and measurable enterprise outcomes.

  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture 

 

Required qualifications, capabilities, and skills
 
 
  • Formal training or certification on software engineering concepts and 5+ years applied experience. 
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Advanced in one or more programming language(s)
  • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
  • Proficient in all aspects of the Software Development Life Cycle
  • Strong hands-on data engineering stack: Apache Spark (batch optimization, partitioning, shuffle tuning, reliability), Apache Airflow (DAG design, backfills, alerting, operational reliability, CI patterns), and Apache Iceberg (schema evolution, partition specs, snapshots, compaction).
  • Proven applied AI/ML and GenAI delivery with measurable impact (RAG, extraction, summarization, ranking/classification, copilots, evaluation) and demonstrated ability to lead across teams and influence technical standards and execution.
  • Deep distributed systems + production engineering expertise across APIs/microservices, CI/CD, observability, containers/Kubernetes, security, and reliability.
Preferred qualifications, capabilities, and skills
  •  Experience with MCP (Model Context Protocol), Agent Skills, and structured agentic architectures.
  • Strong practical usage of AI engineering productivity tooling (for example, GitHub Copilot, Claude Code) in enterprise SDLC environments.
  • Familiarity with VSI and Cloud Foundry contexts.
  • Advanced Java engineering proficiency in addition to Python.
  • Expert-level Python for production systems (packaging, dependency management, performance); strong Java proficiency is a plus.

 

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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⚡ Skillsi85%
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Ontology Match: 85.0
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Title Fit: 78.00