About this role
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 JP Morgan Chase within the Corporate Technology 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
• Own end-to-end solution architecture across applications, APIs, data, integrations, and environments (development through production). • Convert business requirements into target-state architecture, HLD, and LLD with clear trade-offs and documented decisions. • Define integration patterns and cross-service contracts (REST/gRPC, event-driven), including versioning, compatibility, and SLAs. • Provide hands-on technical leadership by contributing Java/Spring Boot and Python code for reference implementations, critical paths, and POCs through to production. • Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams. • 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 and support capacity unlock initiatives. • Lead design spikes and performance investigations to validate approaches and resolve complex technical risks. • Drive complex production issue triage and root cause analysis, ensuring sustainable fixes and operational learning. • Design and deliver production patterns for AI/ML capabilities (batch and real-time inference, feature pipelines, evaluation, monitoring). • Partner with Data Science teams to move models from experimentation to reliable, scalable, and observable services. • Implement responsible AI controls (traceability, testing/evaluation, approvals, and appropriate human oversight where required). • Define and deliver NFRs and operational excellence (availability, latency, throughput, scalability, resiliency, RTO/RPO, capacity planning, observability/SLOs/runbooks) with secure-by-design and SDLC/audit compliance
Required qualifications, capabilities, and skills
• Formal training or certification on software engineering concepts and 9+ years applied experience. • Strong hands-on experience designing and delivering enterprise solutions end-to-end. • Demonstrated solution architecture capability (target-state architecture, HLD/LLD, trade-offs, and architecture decision documentation) across services, data, and integrations. • Java 11/17+ expertise, including Spring Boot microservices, API design, and testing practices. • Python 3.x expertise for services, automation, and data/ML integration. • Proven distributed systems experience: microservices, event-driven architecture, and messaging/streaming (Kafka or equivalent). • Strong data architecture fundamentals: relational/NoSQL patterns (including MongoDB), caching (e.g., Redis), data consistency, and schema evolution. • Practical experience productionizing AI/ML: inference patterns, model packaging/serving, and monitoring/drift fundamentals. • Working knowledge of MLOps concepts and operational model lifecycle management. • DevOps delivery mindset: CI/CD, automated testing (unit/integration/contract), and release/rollback strategies; strong security and resiliency mindset suitable for regulated environments with excellent communication and influence skills to align stakeholders.
Preferred qualifications, capabilities, and skills
• Kubernetes/container platforms and cloud-native patterns (autoscaling, config/secrets, service-to-service security). • GenAI experience (LLMs, RAG, vector search, evaluation frameworks, prompt/model governance) where applicable. • Modernization experience (monolith decomposition, strangler patterns, incremental migration). • Advanced production readiness and observability practices, including readiness reviews, monitoring, and operational rigor.