Software Engineer

Responsibilities:

• Feature Development Ownership: Design, code, test, and deploy key features for our core Java backend services and REST APIs, taking ownership of assigned deliverables from initial design through to production release.
• System Performance and Scalability: Write clean, efficient code optimized for performance and reliability across high-traffic cloud environments. Assist with database query tuning, service optimization, and performance monitoring.
• AI and Agentic Capabilities Support: Collaborate on integrating and extending AI-powered features, including agentic workflows (e. g., tool-using agents, multi-step reasoning systems).
• Cross-Functional Collaboration: Work alongside frontend/data engineers to refine technical specifications, break down tasks, and deliver iterative updates to member-facing products and internal tools.
• Quality and Operational Excellence: Maintain high code quality by writing robust unit and integration tests, participating in peer code reviews, troubleshooting production issues, and monitoring application performance using tools like Datadog.
• Continuous Improvement: Participate in architectural discussions, contribute to technical documentation, and adopt modern tooling and backend best practices, including emerging patterns for agentic AI systems.

Requirements:

• Experience: 4-6 years of professional backend software development experience, with a strong foundation in object-oriented design and cloud-based architecture.
• Core Java Skills: Solid working proficiency in Java (Java 11/17+), including familiarity with collection frameworks, concurrency basics, and modern Java features.
• Spring Ecosystem: Hands-on experience building backend applications using Spring Boot and Spring Data.
• APIs and Messaging: Experience building and consuming RESTful APIs, along with exposure to asynchronous messaging technologies like Apache Kafka and AWS SQS/SNS.
• Databases: Solid understanding of relational databases (PostgreSQL, MySQL), including writing complex queries, alongside exposure to S3 Data Lake. Familiarity with AWS data services such as Amazon Athena and AWS Glue Data Catalog is a plus.
• Cloud and DevOps Basics: Familiarity with AWS services, containerization (Docker), EKS, and CI/CD pipelines (GitHub Actions, Jenkins).
• Testing Practice: Strong commitment to software quality using testing frameworks like JUnit and Mockito.
• Basic AI Knowledge: Foundational understanding of modern AI concepts (LLMs, embeddings, prompt engineering, RAG patterns) and willingness to apply them in backend systems.
• Agentic AI Skills: Exposure to or strong interest in agentic AI patterns, such as tool-calling agents, multi-agent systems, planning/reasoning loops, and integrating AI agents with external tools/APIs via AI frameworks.