Lead Software Engineer-Big Data Python / Java , Databricks

Job Description As a Lead Software Engineer at JPMorganChase within the Corporate Technology Sector, you provide expertise and engineering excellence as an integral part of an agile data engineering team. To enhance, build, and deliver a trusted market leading Global Know Your Customer (KYC) and Risk Assessment Data Platform 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. This role is suited to a senior engineer who has hands-on skills to lead across multiple teams—defining architecture, engineering practices and standards, and delivering high-impact software that scales. Job responsibilities - Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and mentors other engineers - Creates durable, reusable software frameworks and patterns that are leveraged across teams and functions - Designs and governs agentic Artificial Intelligence, systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments - 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. - Establishes engineering standards for Large Language Model-based applications — RAG pipelines, embedding workflows, vector store integrations, and model serving — ensuring safety, observability, and reproducibility at scale - Drives adoption of advanced technical methods and practices aligned with the latest industry standards and product development methodologies - Advises cross-functional teams on technological matters within your domain of expertise - 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 skills - Formal training or certification on software engineering concepts and 5+ years applied experience. - Hands-on practical experience delivering system design, application development, testing, and operational stability at enterprise scale - Hands-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 environments - Expert in one or more programming languages, particularly Python and/or Java - Advanced knowledge of software application development and technical processes, with considerable depth in one or more disciplines (e.g., cloud, AI/ML, data engineering) - 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 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 governance - Practical cloud-native experience (AWS, Azure, or GCP) - Ability to present and effectively communicate with senior leaders and executives Preferred qualifications, capabilities, and skills - Experience with modern data platforms such as Databricks or Snowflake - Deep hands-on experience with Spark/PySpark and other big data processing technologies - Expertise in open-source table formats and catalog services such as Apache Iceberg - Experience 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 workloads.