As a Software Engineer III at JPMorganChase within the Corporate Technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
- Produces architecture and design artifacts for complex applications while being accountable for 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
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts, plus 5+ years of applied experience building production Python systems, including web/API services (Flask or FastAPI) and the ML/NLP ecosystem (scikit-learn, pandas, NumPy).
- Demonstrated experience taking machine learning models from prototype to production - training, packaging, deployment, monitoring, retraining, and decommissioning - in real-world business applications.
- Experience with building LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.
- Proven experience working with large datasets and distributed compute (Spark / Databricks or equivalent), with SQL fluency and an understanding of partitioning, performance, and cost.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
- Working knowledge of LLM application patterns - prompt design, retrieval-augmented generation (RAG), embeddings and vector search, structured output, and tool/function calling.
- Familiarity with MCP (Model Context Protocol), Agent to Agent(A2A) Agent Skills and architectures that connect models to tools/data through standardized interfaces.
- Overall knowledge of the Software Development Life Cycle, and a solid understanding of agile delivery practices including CI/CD, Application Resiliency, and Security.
Preferred qualifications, capabilities, and skills
- Production experience with Databricks (Delta Lake, Unity Catalog, MLflow, Databricks Jobs) and workflow orchestration with Apache Airflow.
- Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge, human-in-the-loop review, red teaming).
- Solid grounding in data pre-processing, feature engineering, model selection, hyper-parameter tuning, and evaluation, including choosing appropriate metrics for imbalanced and unlabeled problems.
- Experience with AWS Bedrock, SageMaker (or equivalent managed ML/GenAI platforms), ECS and deployment patterns for scalable inference.
- Experience with developer productivity tooling such as GitHub Copilot and Claude Code, paired with strong SDLC controls.
- Knowledge of the financial services industry and operating in regulated environments (auditability, controls, data handling).
- Familiarity with financial risk domain concepts - market, credit, counterparty, or investment risk, portfolio exposure, and data-quality controls.