About The Role

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.