Lead Software Engineer-Big Data Python / Java , Databricks

JPMorgan Chase & Co.; Houston (TX); On-site; USD 140,000 - 170,000; Full time

Job summary

JPMorgan Chase & Co. in Houston, TX seeks a Lead Software Engineer to guide the design and delivery of a trusted Global KYC and Risk Assessment Data Platform. You will architect, mentor, and ship high-impact software across teams in a regulated financial services environment.

The role focuses on AI-enabled engineering practices, secure coding, and scalable cloud-native solutions, requiring senior hands-on leadership and deep expertise across data, AI/ML, and platforms.

Qualifications

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands-on experience delivering system design, application development, testing, and operational stability at enterprise scale.
• Experience designing and deploying production AI/ML systems, including LLM-based apps and agentic architectures in regulated environments.
• Expert in Python or Java with strong software development fundamentals.
• Advanced knowledge of software development processes with cloud, AI/ML, or data engineering focus.
• Experience leading enterprise‑grade AI‑assisted software development tools and setting team expectations for validating AI outputs.

Responsibilities

• Develops secure, production-grade code for data-intensive applications and mentors engineers.
• Creates durable software frameworks and patterns used across teams.
• Designs and governs AI-enabled data platforms with multi-agent workflows and tool integrations.
• Drives governance and adoption of AI-assisted engineering practices across teams to improve quality and delivery.
• Applies SDLC tools and AI-assisted development to improve automation and efficiency at scale.
• Establishes standards for LLM-based applications, including RAG pipelines and model serving, with safety and observability.
• Advises cross-functional teams on architectural and technical decisions within data engineering and AI domains.
• Promotes responsible AI usage, data sensitivity, and secure handling of inputs/outputs.

Skills

Python

Java

AI/ML

Cloud

Data engineering

Leadership

Education

Software engineering degree

Tools

Kafka

Redis

Dynatrace

Grafana

Airflow

Temporal

Spark

Databricks

Job description

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.