Lead Software Engineer Java Spring boor Gen AI
JPMorgan Chase4 days ago
Mumbai, IndiaStaff+
Responsibilities
- Own end-to-end solution architecture across applications, APIs, data, integrations, and development-to-production environments.
- Translate business requirements into target-state architecture, high-level designs, low-level designs, trade-offs, and documented decisions.
- Define integration patterns and cross-service contracts, including versioning, compatibility, and service-level agreements.
- Provide hands-on technical leadership by developing Java/Spring Boot and Python reference implementations, critical paths, and proofs of concept through production.
- Lead design spikes, performance investigations, production issue triage, and root cause analysis.
- Establish reusable engineering patterns, templates, shared components, and technical standards.
- Design and deliver production AI/ML patterns for batch and real-time inference, feature pipelines, evaluation, and monitoring.
- Partner with Data Science teams to productionize reliable, scalable, and observable model services.
- Implement responsible AI controls covering traceability, testing, evaluation, approvals, and human oversight.
- Define nonfunctional requirements and operational practices for availability, latency, throughput, scalability, resiliency, recovery objectives, capacity, observability, service objectives, runbooks, security, and audit compliance.
Requirements
- Formal training or certification in software engineering concepts and 9+ years of applied experience.
- Strong hands-on experience designing and delivering enterprise solutions end to end.
- Expertise in Java 11/17+ with Spring Boot microservices, API design, and testing practices.
- Expertise in Python 3.x for services, automation, and data/ML integration.
- Proven distributed systems experience with microservices, event-driven architecture, and Kafka or equivalent messaging/streaming technology.
- Strong data architecture fundamentals, including relational and NoSQL patterns, caching such as Redis, data consistency, and schema evolution.
- Practical experience productionizing AI/ML through inference patterns, model packaging and serving, and monitoring and drift fundamentals.
- Working knowledge of MLOps and operational model lifecycle management.
- Experience with CI/CD, automated unit/integration/contract testing, and release/rollback strategies.
- Strong security and resiliency mindset for regulated environments, along with communication and stakeholder influence skills.
- Preferred experience with Kubernetes, container platforms, cloud-native patterns, autoscaling, configuration and secrets, and service-to-service security.
- Preferred GenAI experience with LLMs, RAG, vector search, evaluation frameworks, and prompt/model governance.
- Preferred modernization experience involving monolith decomposition, strangler patterns, and incremental migration.
- Preferred advanced production-readiness and observability practices, including SRE-style monitoring and operational rigor.
Tech Stack
Categories
About JPMorgan Chase
JPMorgan Chase provides consumer and commercial banking, payments, credit card, wealth management, and corporate and investment banking services to individuals, businesses, institutions, and governments. The public company (NYSE: JPM) earns revenue from interest, fees, trading, and asset management across operations in more than 100 markets. Headquartered in New York City with roots dating to 1799, it serves retail customers and prominent corporate and government clients through brands including Chase and J.P. Morgan.