About the Role
Lead the design and delivery of production-grade Java services using frameworks like Spring Boot and containerization platforms such as AWS Fargate Serve as the technical authority for frontend development using Angular, focusing on efficient component architecture, RxJS, and routing strategies Architect and oversee the implementation of AWS CDK stacks, including infrastructure for Fargate tasks, ALB routing, Lambda functions, and CloudFront distributions Provide strategic guidance on serverless workflows, including authoring Lambda functions and Lambda[redacted] logic for authentication, CDN-layer functionality, and other edge use cases Oversee CI/CD pipelines in GitLab, including automating workflows, reviewing merge requests, and ensuring best practices in deployment processes Utilize AI tools like Claude Code to streamline specification, implementation, and iteration processes, integrating AI into daily development workflows Ensure the development and maintenance of AI context packs to keep aligned with evolving codebases and domain-specific knowledge Lead sprint planning, reviews, and team discussions to ensure alignment with project goals and delivery timelines Mentor developers on technical implementation, AI tool adoption, and best practices for full-stack development Advocate for quality assurance through rigorous code reviews, testing practices, and adherence to architectural standards Requirements 5+ years of full-stack engineering experience with a strong focus on backend Java services and cloud-native architecture At least 1 year of relevant leadership experience Expertise in Java with senior-level production experience in Spring Boot, including deploying scalable containerized services in cloud environments like AWS Proficiency in Angular development, including RxJS, routing mechanisms, and SPA deployments via S3 and CloudFront Advanced knowledge of AWS services, including Fargate, Lambda, API Gateway, and infrastructure as code using AWS CDK Familiarity with managing and improving CI/CD pipelines in GitLab, with experience automating workflows and promoting deployments across environments Background in writing and deploying serverless solutions using Lambda and Lambda[redacted] for edge logic and CDN functionality Flexibility to use IntelliJ IDEA for Java and VS Code for Angular and AWS infrastructure development Proven ability to mentor and guide development teams to leverage AI tools like Claude Code effectively for collaborative software delivery English language proficiency at an Upper-Intermediate level (B2) or higher Nice to have Understanding of domain-specific context management for optimizing AI-assisted workflows Showcase of past work using AI tools like Claude Code or similar for production-ready software delivery Familiarity with alternative AI tools like Kiro for development support Background in designing user experience workflows for web-based applications Knowledge of best practices for embedded engineering within collaborative team environments
EPAM is a psychological theory of learning and memory implemented as a computer program. Originally designed by Herbert A. Simon and Edward Feigenbaum to simulate phenomena in verbal learning, it has been later adapted to account for data on the psychology of expertise and concept formation. It was influential in formalizing the concept of a chunk. In EPAM, learning consists in the growth of a discrimination network.
EPAM was written in IPL/V.
