Senior Software Developer
Summary
Builds and maintains scalable Angular front-ends with Python/FastAPI back-ends, Docker/Kubernetes microservices, and CI/CD pipelines for enterprise clients.
- Master's or Bachelor's degree in Computer Science/Information Technology/Programming & Systems Analysis/Science (Computer Studies) faculties.
- At least 12 years of software development experience, including 8+ years of front-end development
- Front-End Expertise:
- Advanced proficiency inAngular 19+
- Deep knowledge ofAGGrid,NgRxstore, Angular signals, and signal store
- Strong sensitivity todesign and UX principles
- Experience conducting code reviews and enforcing Angular development best practices
- Ability to ensure consistency and homogeneityacross front-end implementations
- Development of automatedBDD tests
- Back-End Knowledge:
- Experience withPython, REST APIs,andFastAPI
- Ability to develop and maintain back-end services as needed
- DevOps & Infrastructure:
- Strong knowledge ofDocker, Kubernetes, andmicroservices architecture
- Experience withGitOps,ArgoCD, Maven,and Git
- Proficiency withCI/CD tools(GitLab, Jenkins, Ansible)
- Familiarity withmonitoring and alertingtools(Elasticsearch)
- Cloud & Architecture:
- Experience withcloud-native architectureandAWS
- Understanding of distributed systems andscalability patterns
- Technical Fundamentals:
- Good knowledge ofalgorithms and data structures, with strongfundamentals incomplexity analysis
- Strong ability toanalyze code– understand execution flow anddebug even without access to a debugger
- Proficiency withstatic analysis tools(SonarQube)
- Good knowledge ofSQL(PostgreSQL, Redis) or SQL-inspireddialects (e.g., HQL)
- Experience writing and maintainingintegration tests
- Collaboration Tools:
- Experience withJiraor similarissue-tracking systems
- Commitment tosoftware craftsmanshipandDevOps culture
- Demonstrated ability to effectively utilize AI-powered tools (e.g., GitHub Copilot) to enhance productivity andproblem-solving capabilities
- Understanding of AI/ML fundamentals including prompt engineering, model limitations, and best practices for human-AI collaboration
- Experience in evaluating AI-generated outputs foraccuracy, security, and alignment with business requirements
- Ability to identify opportunities for AIintegration and automation within existing workflows and processes