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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer - AI Engineer - **Company:** JPMorgan Chase & Co. - **Location:** Wilmington, DE, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Automation of Tests, Microsoft Azure, Cloud Engineering, Software Quality, Continuous Integration, Python (Programming Language), Metadata, Performance Tuning, Tensorflow, Software Deployment, Software Engineering, Unstructured Data, Management of Software Versions, Pytorch, Large Language Models, Prompt Engineering, Machine Learning Operations, Human in the Loop, Microservices - **Published:** September 3, 2026 - **Apply:** https://www.themuse.com/jobs/jpmorganchase/principal-software-engineer-ai-engineer ## About the Role * Formal training or certification on software engineering concepts and 7+ years applied experience * Strong Python engineering skills; experience with PyTorch or TensorFlow * Expertise working with Vector storage systems and designing memory for Agents * Expertise developing long running agents that run autonomously using tools, skills and human in the loop * Proven experience deploying LLM-backed services to production (APIs, microservices) * Deep MLOps experience, including CI/CD, monitoring, incident response, and model governance * Cloud-native AI deployment experience (AWS or Azure), with cost and performance optimization * Demonstrated commitment to responsible AI practices and operational excellence * Strong communication and collaboration skills, working across product, risk, legal, and compliance teams * Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data. * Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse. Preferred Qualifications, Capabilities, and Skills: * Experience with fine-tuning, adapters, or custom evaluation frameworks * Background operating AI systems in regulated environments (finance, healthcare, etc.) * Experience with prompt engineering and LLM orchestration * Knowledge of safety filters, audit logging, and explainability in production systems * Experience mentoring senior engineers and leading architecture discussions * Demonstrated ability to influence technical roadmaps and priorities FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries. ## Description * Design and implement agentic AI reference architectures, including orchestration, retrieval, memory, guardrails, and evaluation harnesses. * Write production-quality Python code (PyTorch or TensorFlow as needed) and review critical-path code * Create reusable components for prompt management, evaluators, safety filters, connectors, embeddings pipelines, and memory stores * Build and operate LLM-powered APIs and microservices integrated into advisor, client, and internal workflows * Own the end-to-end ML lifecycle: experimentation, CI/CD, automated testing, monitoring, drift detection, versioning, and rollback * Optimize inference for latency, throughput, caching, batching, model selection, and cost per inference * Partner with data teams on structured and unstructured data pipelines, document ingestion, metadata, and access controls * Set engineering standards for agentic AI systems and lead design reviews * Influence roadmap and priorities through technical insight and delivery * Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams. * 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 at scale., Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The Prompt Engineer ✍️](https://www.wearedevelopers.com/magazine/216-the-prompt-engineer)