{"id":3860087,"url":"https://alion.io/job/j-p-morgan-workplace-solutions-software-engineer-ii-python-cibt","title":"Software Engineer II - Python - CIBT","company":{"id":1848842,"name":"J.P. Morgan Workplace Solutions","domain":"jpmorganworkplacesolutions.com","url":"https://alion.io/company/jpmorganworkplacesolutions","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Buenos Aires, Argentina"],"countries":["AR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Function Calling","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Embeddings","optional":true},{"name":"Semantic Search","optional":true}],"status":"live","first_seen_at":"2026-11-05T13:21:52Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T16:35:54Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"The CIBT ATA group has been created to actively manage the risks, costs and associated size constraints of the JPMorganChase retained portfolio in an active and connected manner. It supports the strategic agenda of our franchise through a coordinated approach to pricing and management of financial resources.\nAs an Experienced Software Engineer at JPMorganChase within the Global Technology team, you serve as a member of an agile team to design and deliver trusted, market-leading technology products in a secure, stable, and scalable way. You will contribute to building modern data- and AI-enabled applications, with an AI-first mindset -including the ability to leverage prompting techniques, LLM-driven workflows, and human-in-the-loop patterns to accelerate delivery and improve user outcomes while meeting strong security, control, and quality standards.\nJob Responsibilities\nParticipates in the design and development of scalable and resilient systems using Python, SQL, React, AWS and other technologies to contribute to continual, iterative improvements for product teams\nExecutes software solutions including design, development, and technical troubleshooting across the full stack where needed\nPartners with internal clients for requirements gathering, troubleshooting, and delivery of tools that enable better strategy, decisioning, and risk/cost management\nCreates secure, high-quality production code and maintains services and algorithms that run reliably with appropriate upstream/downstream systems\nProduces or contributes to architecture and design artifacts for applications while ensuring design constraints are met by software code development\nGathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems\nIdentifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene, data quality, and system architecture\nContributes to software engineering communities of practice and events that explore new and emerging technologies, including modern AI engineering patterns\n AI-first / prompt-oriented development responsibilities (embedded into delivery):Designs and implements LLM-enabled features (e.g., summarization, extraction, Q&A, reasoning assistants) with attention to reliability, latency, cost, and user experience\nPractices prompt-oriented development: iterating on prompts and system instructions as first-class artifacts, using versioning, test cases, and measurable evaluation criteria (quality, groundedness, safety)\nImplements patterns such as retrieval-augmented generation (RAG), tool/function calling, workflow orchestration, and human-in-the-loop review where appropriate\nBuilds evaluation and monitoring approaches for AI features (automated checks, regression tests, guardrails, observability) and continuously improves quality based on production feedback\nApplies secure engineering practices to AI-enabled systems, including data minimization, access controls, safe handling of sensitive information, and resilience against prompt injection and misuse\nAdds to team culture of diversity, opportunity, inclusion, and respect\n\nRequired qualifications, capabilities, and skills\nHands-on practical experience in system design, application development, testing, and operational stability\nProficient in coding in Java or Python\nExperience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages\nOverall knowledge of the Software Development Life Cycle\nUnderstanding of agile methodologies such as CI/CD, application resiliency, and security\nKnowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)\nDemonstrated ability to deliver AI-enabled capabilities responsibly, including comfort working with prompting techniques, structured inputs/outputs, evaluation approaches, and iterative refinement in partnership with product and users\nPreferred qualifications, capabilities, and skills\nFamiliarity with modern front-end technologies (e.g., React)\nExposure to cloud technologies (e.g., AWS)\nKnowledge and interest in the financial industry\nExperience with applied AI engineering practices (any of the following): RAG, semantic search, embeddings, prompt/version management, LLM evaluations, model/service 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