Software Engineer

Senior Lead Software EngineerBe an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Technology Data Strategy & Architecture organization, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.Job ResponsibilitiesDevelops secure, high-quality production code for data-intensive applications and platforms, and reviews and debugs code written by othersLeads end-to-end design and implementation of complex software features, from requirements through deployment and operational stabilityDrives technical decisions that influence application design, functionality, performance, and reliabilityBuilds and maintains agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environmentsImplements LLM-based applications including RAG pipelines, embedding workflows, vector store integrations, and model serving infrastructureOwns observability, evaluation, and safety of production AI systems — including prompt monitoring, output validation, cost tracking, and latency optimizationIdentifies and executes opportunities to automate remediation of recurring issues and improve operational stabilityExecutes creative software solutions, including design, development, and technical troubleshooting to solve complex and ambiguous problemsMentors and coaches junior and mid-level engineers, conducting code reviews and sharing engineering best practicesContributes to firmwide frameworks, tools, and SDLC practices as an engaged member of the engineering communityDrives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved 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 5+ years applied experienceHands-on experience building and shipping LLM-based applications and agentic systems with tool use, memory, and multi-step reasoning in production environmentsAdvanced proficiency in one or more programming languages, particularly Python and/or JavaDeep experience with large-scale data processing, microservices, API design, and event streaming (Kafka)Working knowledge of relational and NoSQL databases, vector stores, and data lake architecturesExperience with caching technologies (Redis, MemCached), observability tools (Dynatrace, Splunk, Grafana), and orchestration frameworks (Airflow, Temporal)Proficiency in CI/CD, test-driven development, automation, and all aspects of the Software Development LifecycleStrong understanding of agile methodologies, application resiliency, and security best practicesPractical cloud-native engineering experience (AWS, Azure, or GCP)Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and securityStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.Preferred Qualifications, Capabilities, and SkillsExperience with LLM orchestration frameworksHands-on experience with 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 monitoringUnderstanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflowsExperience with modern data platforms such as Databricks or SnowflakeHands-on experience with Spark/PySpark and big data processing at scaleKnowledge of the financial services industry and its technology systems - Awareness of AI risk and regulatory considerations relevant to AI use in financial decision-making