About the role
from listingCarry out critical tech solutions across multiple technical areas as an integral part of an agile team.
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, OPS Tech, 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:
Design and develop creative full-stack software solutions using innovative approaches aligned to business outcomes.
Build and implement AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
Architect and deliver cloud-native microservices and APIs with strong security controls, scalability, resilience, and performance.
Drive team adoption of enterprise-authorized AI-assisted engineering practices (e.g., CoPilot, Claude) to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns.
Mentor and develop engineers through code reviews, pairing, technical guidance, and feedback; raise the bar on engineering excellence and continuous improvement.
Establish and own engineering standards across the team (architecture patterns, coding standards, testing strategy, operational readiness).
Define and operate service reliability practices (SLIs/SLOs, incident response, root-cause analysis, postmortems, and continuous reliability improvements).
Identify recurring operational issues and automate remediation to improve system stability and observability (logs/metrics/tracing).
Build and maintain CI/CD pipelines and release strategies (quality gates, canary/blue-green, rollback/feature flags) to enable safe, predictable delivery.
Communicate status and risks clearly; manage priorities and dependencies across multiple initiatives; collaborate in a Scrum team and participate in Agile ceremonies.
Deliver production code primarily in Java (and Python as needed) using AWS (e.g., ECS) and related technologies.
Required qualifications, capabilities, and skills:
Formal training or certification in Software Engineering and 5+ years of applied experience building production systems.
Strong system design, application development, and operational stability skills in production environments (including on-call/incident ownership).
Expertise designing and building microservices and RESTful APIs, with strong knowledge of data technologies and integration patterns.
Hands-on experience building cloud-native systems on at least one major cloud platform (AWS, GCP, or Azure).
Experience with distributed systems concepts (scalability, resiliency, concurrency, idempotency, failure modes, and performance tuning).
Proven ability to influence product design and technical direction by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment, with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Demonstrated experience leading effective team use of approved AI-assisted development practices (coding, code review, test acceleration, troubleshooting), including setting expectations and reusable validation patterns.
Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, summarization/extraction, workflow assistants), including evaluation and monitoring considerations.
Understanding of responsible AI use in engineering workflows (data sensitivity, safe handling of inputs/outputs, resiliency/security expectations) and ability to guide peers on safe and effective usage.
Strong communication and stakeholder management skills; ability to deliver across multiple initiatives and drive outcomes through ambiguity.
Preferred qualifications, capabilities, and skills:
AWS cloud certification(s).
AI/ML or GenAI certification(s) (or equivalent practical experience delivering LLM-enabled capabilities).
Advanced Java proficiency (primary) and working knowledge of Python.
Experience with Docker, Kubernetes, and modern CI/CD practices.
Experience with multi-region deployments, disaster recovery, and zero-downtime release patterns.
Strong understanding of responsible AI practices, including prompt/agent safety patterns and secure handling of sensitive data.
Track record delivering scalable, reliable, and secure products from concept to launch in a regulated environment.
Domain familiarity with financial services technology; IVR/CCaaS experience a plus.