We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

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, spaCy, PyTorch or TensorFlow).
• Demonstrated experience taking machine learning models from prototype to production - training, packaging, deployment, monitoring, retraining, and decommissioning - in real-world business applications.
• 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.
• Experience with agentic AI frameworks - multi-agent orchestration, planning and tool use, and integration patterns such as the Model Context Protocol (MCP); familiarity with Google ADK, Arize Phoenix SDK, Claude skills is a must.
• 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.
• Strong working knowledge of both supervised and unsupervised techniques, including tree-based and kernel methods (gradient boosting, random forest, SVM) and anomaly-detection approaches such as Isolation Forest, Local Outlier Factor, and locality-sensitive hashing.
• Solid grounding in data pre-processing, feature engineering, model selection, hyper-parameter tuning, and evaluation, including choosing appropriate metrics for imbalanced and unlabeled problems.
• Applied NLP experience - text-to-SQL / natural-language query, entity extraction and linking, relevancy ranking, summarization, and news or research feed analytics; exposure to computer vision or multi-modal document processing (OCR, layout-aware extraction) is a plus.
• Familiarity with AWS machine learning services such as Amazon Bedrock with EKS-based deployment.
• Knowledge of deep learning architectures (CNNs, transformers, sequence models) and of reinforcement learning concepts and their practical applications.
• Experience building self-service ML tooling - model catalogues, automated pipelines, feature stores, and explainability (SHAP, LIME) surfaced to non-technical users.
• Understanding of industry-standard validation and testing for LLMs - ground-truth evaluation datasets, accuracy, hallucination and toxicity metrics, guardrails and content moderation, red-teaming, and embedding evals into CI/CD.
• Experience with AI/ML observability (OpenTelemetry, Phoenix, or equivalent tracing) and with model governance, auditability, and control requirements in a regulated financial-services environment. Familiarity with financial risk domain concepts - market, credit, counterparty, or investment risk, portfolio exposure, and data-quality controls.