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.