Software Engineer II - Python - CIBT

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. As 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. Job Responsibilities Participates 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 Executes software solutions including design, development, and technical troubleshooting across the full stack where needed Partners with internal clients for requirements gathering, troubleshooting, and delivery of tools that enable better strategy, decisioning, and risk/cost management Creates secure, high-quality production code and maintains services and algorithms that run reliably with appropriate upstream/downstream systems Produces or contributes to architecture and design artifacts for applications while ensuring design constraints are met by software code development Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems Identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene, data quality, and system architecture Contributes to software engineering communities of practice and events that explore new and emerging technologies, including modern AI engineering patterns 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 Practices prompt-oriented development : iterating on prompts and system instructions as first-class artifacts, using versioning, test cases, and measurable evaluation criteria (quality, groundedness, safety) Implements patterns such as retrieval-augmented generation (RAG) , tool/function calling, workflow orchestration, and human-in-the-loop review where appropriate Builds evaluation and monitoring approaches for AI features (automated checks, regression tests, guardrails, observability) and continuously improves quality based on production feedback Applies secure engineering practices to AI-enabled systems, including data minimization, access controls, safe handling of sensitive information, and resilience against prompt injection and misuse Adds to team culture of diversity, opportunity, inclusion, and respect Required qualifications, capabilities, and skills Hands-on practical experience in system design, application development, testing, and operational stability Proficient in coding in Java or Python Experience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages Overall knowledge of the Software Development Life Cycle Understanding of agile methodologies such as CI/CD, application resiliency, and security Knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) Demonstrated 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 Preferred qualifications, capabilities, and skills Familiarity with modern front-end technologies (e.g., React) Exposure to cloud technologies (e.g., AWS) Knowledge and interest in the financial industry Experience with applied AI engineering practices (any of the following): RAG, semantic search, embeddings, prompt/version management, LLM evaluations, model/service integration patterns, or AI feature monitoring in production