Lead Software Engineer - AI/ML Lead Software Engineer
Jpmc Cx · Plano, Texas, United States
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking-Home Lending Servicing group, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure and high-quality production code, and reviews and debugs code written by others
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- 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
- Designs and delivers scalable ML systems (batch and real-time inference), including data/feature pipelines, model training, evaluation, deployment, monitoring, and drift/performance management
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and ML systems (alerts, SLOs, auto-rollbacks, guardrails)
- Leads communities of practice across Software Engineering and AI/ML to drive awareness and use of new and leading-edge technologies (MLOps, LLM patterns, feature stores, observability, model monitoring)
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience, including designing and delivering production-grade distributed systems; 2+ years building and/or operating ML-enabled systems in production
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s) ) (e.g., Python, Java) and strong CS fundamentals (data structures, algorithms, concurrency)
- Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Demonstrated proficiency in software applications and technical processes within AI/ML (e.g., model training pipelines, feature engineering, experiment tracking, model deployment, monitoring/observability)
- Experience with data and ML tooling such as Spark, SQL, orchestration (e.g., Airflow-like), containerization (Docker), and Kubernetes-based deployment patterns
- Practical cloud native experience (e.g., AWS), including building secure services, secrets management, networking basics, and scalable data processing
- Ability to communicate complex technical concepts clearly and influence architecture decisions across teams
Preferred qualifications, capabilities, and skills- Experience applying ML to servicing or customer operations use cases (e.g., document understanding, classification, forecasting, contact center assist, workflow optimization)
- Strong MLOps experience (e.g., MLflow-like tooling, model registries, feature stores, canary/shadow deployments, model performance/drift monitoring)
- Experience with Responsible AI practices (bias/fairness testing, explainability, privacy-aware design) and working with risk/control partners in regulated environments
- Familiarity with LLM-enabled architectures (RAG patterns, prompt/version management, evaluation, safety filters) and deploying them with enterprise controls
- Experience building event-driven and streaming architectures for near-real-time ML signals
- Mentoring/coaching experience and a track record of raising engineering quality via standards, reviews, and reusable frameworks