Lead Software Engineer - AI/ML Lead Software Engineer at JP Morgan Chase – Plano | Jobyssey

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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

Lead Software Engineer - AI/ML Lead Software Engineer

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