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The Role
Develop and productionize Python-based machine learning and NLP systems, including APIs, distributed data processing, model lifecycle management, LLM applications, and agentic AI frameworks. Build secure, scalable software and architecture, validate AI-assisted development outputs, and improve systems using data insights. Responsibilities include responsible AI practices, observability, testing, governance, CI/CD, and collaboration in an agile financial-services environment.
Summary Generated by Built In
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
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.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Skills Required
- Formal training or certification in software engineering concepts
- 5+ years of applied experience building production Python systems
- Experience with web/API services using Flask or FastAPI
- Experience with the ML/NLP ecosystem, including scikit-learn, pandas, NumPy, spaCy, PyTorch, or TensorFlow
- Experience taking machine learning models from prototype to production, including training, packaging, deployment, monitoring, retraining, and decommissioning
- Experience with large datasets and distributed compute such as Spark or Databricks
- SQL fluency and understanding of partitioning, performance, and cost
- Hands-on experience using enterprise-authorized AI-assisted software development tools and validating their outputs
- Understanding of responsible AI use, data sensitivity, secure input/output handling, resiliency, and security expectations
- Working knowledge of LLM application patterns, including prompt design, RAG, embeddings, vector search, structured output, and tool/function calling
- Experience with agentic AI frameworks, multi-agent orchestration, planning, tool use, and MCP integration
- Familiarity with Google ADK, Arize Phoenix SDK, and Claude skills
- Knowledge of the software development life cycle, agile delivery, CI/CD, application resiliency, and security
- Production experience with Databricks, including Delta Lake, Unity Catalog, MLflow, and Databricks Jobs
- Experience with Apache Airflow workflow orchestration
- Knowledge of supervised and unsupervised machine learning techniques and anomaly detection
- Experience with data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation
- Applied NLP experience with text-to-SQL, entity extraction, relevancy ranking, summarization, and feed analytics
- Exposure to computer vision or multimodal document processing, including OCR and layout-aware extraction
- Familiarity with AWS machine learning services, including Amazon Bedrock and EKS-based deployment
- Knowledge of deep learning architectures and reinforcement learning concepts
- Experience building self-service ML tooling, model catalogs, automated pipelines, feature stores, and explainability tools
- Understanding of LLM validation, evaluation datasets, hallucination and toxicity metrics, guardrails, moderation, red-teaming, and CI/CD evaluation
- Experience with AI/ML observability, model governance, auditability, and controls in regulated financial services
- Familiarity with financial risk concepts and data-quality controls
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
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The Company
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
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