Principal Software Engineer

Principal Software EngineerIf you are looking for a game-changing career, working for one of the world's leading financial institutions, you've come to the right place.As a Principal Software Engineer at JPMorgan Chase within the Corporate Sector, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm's portfolios.Job responsibilitiesArchitects and implements complex, scalable engineering frameworks and solutions using modern software design principlesDevelops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineersCreates durable, reusable software frameworks and patterns that are leveraged across teams and functionsDesigns and governs agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environmentsEstablishes engineering standards for LLM-based applications — RAG pipelines, embedding workflows, vector store integrations, and model serving — ensuring safety, observability, and reproducibility at scaleDrives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologiesServes as the function's go-to subject matter expert in one or more areas of focus within data engineering, platform architecture, or AI systemsAdvises cross-functional teams on technological matters within your domain of expertiseInfluences leaders and senior stakeholders across business, product, and technology teams on technical strategy and directionArchitects 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.Required qualifications, capabilities, and skillsFormal training or certification on software engineering concepts and 7+ years applied experienceHands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scaleHands-on experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures with tool use, memory, and multi-step reasoning in regulated environmentsExpert in one or more programming languages, particularly Python and/or JavaAdvanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering)Experience in large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governancePractical cloud-native experience (AWS, Azure, or GCP)Ability to present and effectively communicate with senior leaders and executivesExperience in Computer Science, Computer Engineering, Mathematics, or a related technical fieldDemonstrated 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 skillsExperience with LLM orchestration frameworks and model serving infrastructure or managed endpoints (AWS Bedrock, Azure OpenAI)Familiarity with AI evaluation and observability practices: evals frameworks, red-teaming, prompt drift detection, and cost/latency monitoring for LLM workloadsUnderstanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflowsExperience with modern data platforms such as Databricks or SnowflakeDeep hands-on experience with Spark/PySpark and other big data processing technologiesExpertise in open-source table formats and catalog services such as Apache IcebergAwareness of AI risk and regulatory considerations relevant to AI use in financial decision-making