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Sr Lead Software Engineer
lead · Technology / Software Development
Be 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 responsibilities
- Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and debugs code written by others
- Leads end-to-end design and implementation of complex software features, from requirements through deployment and operational stability
- Drives technical decisions that influence application design, functionality, performance, and reliability
- Builds and maintains agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments
- Implements LLM-based applications including RAG pipelines, embedding workflows, vector store integrations, and model serving infrastructure
- Owns observability, evaluation, and safety of production AI systems — including prompt monitoring, output validation, cost tracking, and latency optimization
- Identifies and executes opportunities to automate remediation of recurring issues and improve operational stability
- Executes creative software solutions, including design, development, and technical troubleshooting to solve complex and ambiguous problems
- Mentors and coaches junior and mid-level engineers, conducting code reviews and sharing engineering best practices
- Contributes to firmwide frameworks, tools, and SDLC practices as an engaged member of the engineering community
- Drives 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 skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Hands-on experience building and shipping LLM-based applications and agentic systems with tool use, memory, and multi-step reasoning in production environments
- Advanced proficiency in one or more programming languages, particularly Python and/or Java
- Deep 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 architectures
- Experience 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 Lifecycle
- Strong understanding of agile methodologies, application resiliency, and security best practices
- Practical 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 security
- Strong 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.
- Experience with LLM orchestration frameworks
- Hands-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 monitoring
- Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows
- Experience with modern data platforms such as Databricks or Snowflake
- Hands-on experience with Spark/PySpark and big data processing at scale
- Knowledge of the financial services industry and its technology systems
- Awareness of AI risk and regulatory considerations relevant to AI use in financial decision-making