{"id":2973381,"url":"https://alion.io/job/jpmorganchase-lead-software-engineering-python","title":"Lead Software Engineering - Python","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":91,"open_postings":1139,"ghost_share":0.007,"stale_share":0.52,"repost_share":0.056,"time_to_fill_p50_days":9,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Jersey City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":176000,"max_usd":352000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1150},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Angular","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Django","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"JavaScript","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"React.js","optional":false},{"name":"Scrum","optional":false},{"name":"Spring Boot","optional":false},{"name":"SQL","optional":false},{"name":"TypeScript","optional":false},{"name":"WebSockets","optional":false},{"name":"Java","optional":true}],"status":"live","first_seen_at":"2026-07-15T13:30:09Z","employer_posted_date":"2026-07-15","last_verified_at":"2026-10-24T17:49:54Z","board_verified":true,"closed_at":null,"days_open":101,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":100},"description":"Be an integral part of an agile team that’s constantly pushing the envelope to enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way.\nAs aLead Software Engineer at JPMorganChase within Infrastructure Platforms, you will make a significant business impact through your skills and contributions, applying technical expertise and problem-solving methodologies to address a diverse array of challenges across multiple technologies and applications.\nJob Responsibilities\nExecute creative software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.\nDeliver end-to-end solutions in the form of cloud-native, microservices-based applications, leveraging the latest technologies and best industry practices.\nUse domain modeling techniques to build best-in-class business products, structuring software for clarity, testability, and evolution.\nDevelops secure high-quality production code, and reviews and debugs code written by others.\nIdentifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.\nLeads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies • Adds to team culture of diversity, opportunity, inclusion, and respect.\nDesign and build solutions that avoid single points of failure using scalable architectural patterns.\nDesign and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that ensure zero-downtime releases and optimize data performance.-\n-Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.\n-Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineering concepts and 5+ years of hands-on experience in software engineering, including system design, application development, testing, and operational support.\nProficiency in back-end technologies (e.g., Python, Flask, Django) with experience building microservices-based applications; for full-stack roles, proficiency also includes front-end technologies (e.g., HTML, CSS, JavaScript, Typescript, React, Angular).\nHands-on development experience with Java and Spring/Spring Boot for building and supporting services.\nExperience working with cloud platforms (e.g., AWS, Azure, GCP), distributed systems, and web technologies, including RESTful APIs and web services, WebSockets, and JSON.\nHands-on experience designing and building scalable applications using SQL and NoSQL databases.\nExperience with agile development methodologies (e.g., Scrum) and an understanding of the software development life cycle.\nAdvanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.\nDemonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)\n- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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\nStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; 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