Partner with product managers, UX designers, business analysts, and stakeholders to translate business needs into technical solutions.
Hands-on Engineering
Prototype complex backend and frontend components; contribute to critical modules when needed.
Define and enforce coding standards, design patterns, component libraries, and best practices across the stack.
Troubleshoot complex production issues (API, UI, performance) and lead root cause analysis.
Platform & DevOps
Drive adoption of CI/CD, containerization (Docker/Kubernetes), and cloud platforms (AWS/Azure/GCP) for both backend services and frontend builds.
Define strategies for performance, scalability, resilience, observability, and security across the full stack.
Establish frontend build pipelines, bundling strategies, CDN delivery, and feature-flag frameworks.
Governance
Participate in architecture review boards; ensure compliance with enterprise architecture, security, accessibility (WCAG), and data governance standards.
Evaluate emerging technologies (backend and frontend) and lead PoCs.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 10+ years applied experience
Hands-on Java 11/17+ expertise, including Spring Boot microservices, API design, and testing practices.
Strong experience with Spring ecosystem (Spring Boot, Spring Cloud, Spring Security, Spring Data).
Deep understanding of microservices architecture, REST/gRPC APIs, GraphQL, and event-driven systems (Kafka, RabbitMQ, ActiveMQ).
Databases: RDBMS (Oracle, PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, Redis); strong SQL and data modeling skills.
Proven experience in performance tuning, high availability, and disaster recovery of mission-critical systems.
Demonstrated proficiency in AI, machine learning, or related techniques as applied to architecture or software engineering disciplines.
Excellent communication, stakeholder management, and documentation skills.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Proficient in all aspects of the Software Development Life Cycle. Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
AWS cloud-native patterns (autoscaling, config/secrets, service-to-service security).
Frontend technologies like React, Micro frontend Architecture
