Description :

We are looking for a Software Engineer II to join one of our Scrum teams building AI-native, event-driven services for automotive retail intelligence. This is a core contributor role for engineers with 2- 4 years of experience who are ready to take end-to-end ownership of features, grow their distributed systems skills, and work in a high velocity environment that demands code quality, hexagonal architecture discipline, and genuine curiosity about AI-native engineering.

Key Responsibilities :

- Develop and maintain Java (Spring Boot 3.x) backend services - implementing domain logic, REST APIs, Kafka event consumers/producers, and MongoDB/PostgreSQL persistence adapters following hexagonal architecture patterns.

- Build features across the full backend slice : domain model, port interface, adapter implementation, unit tests, and integration tests.

- Write integration tests using Test containers for MongoDB, PostgreSQL, Kafka, and Elasticsearch adapters - ensuring each adapter is tested independently from the domain core.

- Participate in design discussions, architecture reviews, and code reviews - actively learning from Staff and Senior engineers.

- Investigate and resolve bugs, performance issues, and Kafka consumer lag across services.

- Write clean, well-documented, testable code with consistent application of hexagonal design principles.

- Contribute to golden dataset test scenario design under the guidance of the Product Manager and SDET.

- Operate with ownership and curiosity in a fast-paced, startup environment.

Skills and Experience :

- 2- 4 years of backend engineering experience; Bachelor's or Master's degree in Computer Science or a related field.

- Solid Java fundamentals : object-oriented design, Spring Boot, REST API implementation, and unit testing (JUnit 5).

- Working knowledge of at least one of : MongoDB, PostgreSQL, or Kafka with willingness to develop depth across all three.

- Understanding of hexagonal architecture concepts (ports & adapters) ability to implement adapters under the guidance of senior engineers.

- Familiarity with cloud environments (AWS), containerization (Docker), and CI/CD pipelines.

- Strong problem-solving skills, attention to code quality, and ability to receive and apply feedback quickly.

- Collaborative mindset and clear communication comfortable asking questions and flagging blockers early.

Preferred Skills :

- Exposure to Kafka producers and consumers in a production or learning context.

- Familiarity with Elasticsearch for search and analytics use cases.

- Curiosity about AI/ML systems awareness of what scoring models, embeddings, and LLMs do at a systems level.

- Exposure to Redis or Aerospike for caching patterns.