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Lead Architect - Java, Kubernetes

JPMorgan Chase
Mumbai, IND
Full-time
Lead · 5+ years experience
Onsite
Discovered 4 days ago
awsdockerjavakafkakubernetesredis

Full Job Posting

Job responsibilities

  • Engages technical teams and business stakeholders to discuss and propose technical approaches to meet current and future needs
  • Defines the technical target state of their product and drives achievement of the strategy
  • Leverages enterprise-authorized AI capabilities within the work environment to accelerate architecture discovery and decisioning, with human-in-the-loop validation and appropriate handling of sensitive data.
  • Evaluates recommendations and provides feedback on new technologies, including outcomes-oriented evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and applicability within existing systems and information
  • Executes creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Develops secure and high-quality production code, and reviews and debugs code written by others
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  • Establishes reuse-first AI-enabled engineering patterns and governance across SDLC/toolchain practices, ensuring traceability/auditability, resiliency, and security controls.

Required qualifications, capabilities, and skills

  • Formal training or certification on architecture concepts and 5+ years applied experience
  • Hands-on practical experience delivering system design, application development, testing, and operational stability for large-scale, distributed, mission-critical systems
  • Advanced in one or more programming language(s) Java 17+ with Spring Boot and microservices.
  • Proficient across SDLC execution, including automation/continuous delivery practices and agile methods such as CI/CD, application resiliency, and security
  • Demonstrated proficiency within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) and ability to evaluate current and emerging technologies to recommend future-state solutions
  • Experience applying enterprise-authorized AI capabilities within the work environment in software engineering and architecture workflows with strong validation habits and awareness of data sensitivity.
  • Ability to evaluate and integrate AI-enabled capabilities into cloud-native architectures while meeting resiliency, security, and auditability requirements.
  • In-depth knowledge of the financial services industry and their IT systems
  • Strong knowledge of architecture patterns such as event-driven and streaming (Kafka), CQRS, Saga, domain-driven design and API design. Strong data architecture skills, including data modeling, Oracle and complex SQL, NoSQL, and options such as ODS, data lake, data warehouse, OLTP and OLAP
  • Practical cloud-native experience with AWS (or equivalent), Docker, Kubernetes, infrastructure as code and observability
  • Experience with architecture modeling tools such as C4, UML or Lucid. Ability to independently solve complex design problems, with strong communication skills to simplify complexity and influence senior business and technology stakeholders

Preferred qualifications, capabilities, and skills

  • Domain knowledge of intraday liquidity, cash management, payments or reconciliation, including intraday liquidity monitoring requirements (e.g., BCBS 248)
  • Cloud architecture certification (e.g., AWS Solutions Architect) or TOGAF. Experience with SRE practices (SLOs/SLIs), chaos engineering and FinOps
  • Experience building agentic AI or GenAI applications (e.g., RAG, multi-step orchestration, guardrails, evaluation and monitoring)
  • Experience establishing reference architectures, blueprints, golden paths and architecture review practices. Track record of modernizing legacy platforms in a regulated environment and understanding of hybrid and multi-cloud operating models, and distributed caching (e.g., Redis, Hazelcast)

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