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Lead Java Platform Engineer (Payments Processing) - Senior Vice President

Canada, Mississauga 145100.00 - 217700.00 USD / Year · Job Posted June 10, 2026
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Job Responsibility

  • Actively code in Java developing reusable frameworks while performing system design, architecture reviews, and code reviews for the core cheque payment platform
  • Contribute to the design of distributed, fault-tolerant, real-time systems for high-volume, low-latency transaction processing
  • Write, review, and refactor production-grade code in Java setting a high technical standard for the team
  • Independently design event-driven, microservices-based architectures utilizing REST APIs, Java and React
  • Apply hands-on experience with Kafka, including message schema design, topic partitioning, consumer group strategies, and fault-tolerant processing
  • Design and govern data architecture across Oracle (SQL) and MongoDB (NoSQL), covering schema design, indexing strategies, query optimization, and data consistency patterns
  • Champion continuous development, feature flags, and progressive delivery, contributing to the design and optimization of CI/CD pipelines
  • Produce and review Architecture Decision Records (ADRs) and technical design documents for key platform components
  • Maintain and enforce engineering standards within the scrum team, including Test-Driven Development (TDD), reusable framework development, CI/CD, secure coding, and observability
  • Conduct code reviews for critical platform components, providing detailed technical feedback to enhance team quality and capacity, ensuring the development of scalable and reusable services
  • Drive the adoption of AI-powered quality practices, including leveraging tools like GitHub Copilot for coding, utilizing LLMs (Gemini experience preferred) to improve payment processing quality, and implementing automated test script generation and vulnerability remediation
  • Lead performance engineering initiatives, including profiling JVM-based applications Java identifying performance hotspots, and optimizing critical settlement processing paths
  • Own the security and compliance posture of the cheque payment platform by participating in threat modeling, vulnerability assessments, and secure design reviews
  • Ensure all engineering delivery aligns with Citi Engineering Excellence Standards, including CI/CD, DevOps, cloud-native practices, and modern ways of working
  • Define and own the engineering roadmap for modernizing the cheque payment platform, migrating global components from monolithic mainframe and legacy applications to a reusable microservices architecture
  • Develop strong domain knowledge in the payment lifecycle, settlement, and cheque image processing to inform sound engineering decisions
  • Own the platform's SLA commitments, driving architectural decisions to meet availability, throughput, and latency targets
  • Develop cross-platform integration by governing API contracts, data flows, and system dependencies across the Payments, Settlements, DDA, and Data Lake ecosystems
  • Drive capacity planning and investment prioritization, making informed decisions on platform scaling, technology refresh, and resource allocation
  • Partner with Operations, Risk, Compliance, and Business teams to ensure the platform meets all regulatory requirements and business needs
  • Act as the primary engineering point of contact for the core Cheque Processing pillar, engaging with senior business, technology, and operations stakeholders
  • Clearly communicate platform health, delivery progress, engineering strategy, and risks to senior leadership
  • Partner with Product, Operations, Risk, Finance, and Compliance teams to align engineering priorities with business and regulatory needs
  • Demonstrate the ability to pivot direction to meet timelines and delivery commitments with tactical solutions, while maintaining a clear vision for strategic platform development

Requirements

  • Java: Extensive hands-on experience in high-throughput, production-grade Java engineering, including JVM performance tuning
  • API Development: Hands-on experience building RESTful APIs with frameworks such as Spring MVC or Spring Boot
  • Microservices Architecture: Proven experience designing microservices ecosystems, including service decomposition, API design, inter-service communication, and cloud engineering
  • Event-Driven & Messaging Systems: Deep, hands-on expertise in Kafka, including topic design, partitioning, consumer groups, exactly-once semantics, and high-throughput stream processing
  • High-Performance Computing: Hands-on experience with profiling and optimization, including JVM tuning, garbage collection (GC) optimization, thread management, and memory profiling
  • High Availability & Fault Tolerance: Practical experience designing resilience patterns such as circuit breakers, bulkheads, failover, and disaster recovery
  • Databases: Hands-on expertise in Oracle (SQL) and Microsoft SQL for schema design, query optimization, and indexing, as well as MongoDB (NoSQL) for document modeling, aggregation pipelines, sharding, and replica sets
  • Cloud-Native Engineering: Hands-on experience with OpenShift/AWS, Kubernetes, and Docker for container orchestration, autoscaling, and cloud-native deployment
  • CI/CD & DevOps: Experience designing and owning CI/CD pipelines aligned with enterprise-level Engineering Excellence Standards. Experience with tools like Jenkins and Gradle/Maven
  • Observability: Hands-on experience with distributed tracing, intelligent alerting, and AI-driven observability using tools like Grafana
  • Secure Engineering: Practical experience with threat modeling, vulnerability assessments, and secure design reviews
  • Agile at Scale: Experience with Agile/SAFe delivery at the program level, including PI planning, cross-team dependency management, and delivery governance

Nice to have

  • ML Integration: Hands-on experience with real-time ML model serving, including feature stores, online inference, and model monitoring
  • AI Frameworks: Familiarity with Retrieval-Augmented Generation (RAG) patterns, vector databases (e.g., Pinecone, Weaviate), or agentic AI frameworks (e.g., LangChain, AutoGen)
  • MLOps: Knowledge of MLOps practices such as model versioning, deployment pipelines, and production model monitoring
  • Resilience Testing: Experience with chaos engineering and resilience testing, including fault injection and production readiness reviews
  • Financial Systems: Knowledge of reconciliation and exception handling patterns in settlement workflows
  • LLM Experience: Familiarity with prompt engineering and LLM fine-tuning in a regulated enterprise context
  • Python: Hands-on expertise in Python for data pipelines, AI/ML integration, scripting, and automation
  • Legacy Modernization: Experience with large-scale transformation projects, including migrating mainframe components (COBOL, Assembler) and legacy systems (C++, .NET) to a microservices architecture
  • Strategy: Experience with technology investment governance and engineering budget management

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