Senior Lead Software Engineer Java Spring boor Gen AI
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Key skills for this role
Key Skills for This Role
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Job responsibilities
- Own end-to-end solution architecture across applications, APIs, data, integrations, and environments (dev → prod).
- 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 to production.
- 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.
- Establish and maintain engineering standards through reusable patterns, templates, and shared components.
- Design and deliver production patterns for AI/ML capabilities (batch + real-time inference, feature pipelines, evaluation, monitoring).
- Partner with Data Science teams to move models from experimentation to reliable, scalable, 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.
- 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, 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. Excellent communication and influence skills—able to explain trade-offs and 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/observability practices (SRE-style monitoring, readiness reviews, operational rigor).
About JPMorgan Chase
Global financial services and investment banking firm.
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