Lead Software Engineer-Python , Java ,Gen AI, Architect

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.