About the role
We are looking for a Senior Backend Engineer to design, build, and scale robust backend services that power complex business workflows. This role requires strong ownership, deep Java expertise, a solid grounding in hexagonal architecture, and the ability to collaborate effectively across product, QA, AI/ML, and engineering teams in a fast-moving, AI-native environment.
Responsibilities:
- Design and build Java (Spring Boot 3 x) microservices following hexagonal architecture, with clean domain logic isolated from MongoDB, PostgreSQL, Cosmos DB, and Kafka adapter implementations.
- Develop and maintain REST APIs, Kafka event consumers/producers, and domain service logic for campaign, scoring, attribution, and customer profile workflows.
- Build and manage MongoDB document schemas and PostgreSQL relational schemas for high-throughput, multi-tenant use cases.
- Implement Cosmos DB data models for event and state storage with appropriate partition key and consistency strategies.
- Take end-to-end ownership of components from domain design and hexagonal adapter implementation through deployment and ongoing optimisation.
- Integrate with Elasticsearch for campaign search, customer lookup, and full-text query workloads.
- Configure and tune Redis or Aerospike for frequency cap enforcement, scoring cache, and session state management.
- Identify and resolve performance bottlenecks across services, event pipelines, and data layers.
- Participate actively in design discussions, architecture decisions, and code reviews.
- Mentor junior engineers and act as a role model for code quality, hexagonal design, and AI-native engineering best practices
Requirements:
- 5+ years of backend engineering experience; Bachelor's or Master's degree in Computer Science or a related field.
- Strong Java 21 and Spring Boot 3 x expertise; solid grasp of hexagonal architecture and clean domain modelling.
- Experience with Kafka: consumer/producer patterns, at-least-once delivery, idempotent processing, and event-driven service design.
- Hands-on MongoDB experience: document schema design, indexing, aggregation pipelines, multi-tenant isolation.
- PostgreSQL experience: schema design, query optimisation, migration management.
- Familiarity with Cosmos DB or equivalent distributed key-value / document stores.
- Experience with Elasticsearch for search and analytics workloads.
- Experience with Redis or Aerospike for high-throughput caching patterns.
- Familiarity with cloud-based systems on AWS (EKS, MSK, S3).
- Strong debugging, performance tuning, and system optimisation skills.
- Familiarity with Agile/Scrum development practices and CI/CD pipelines.
Good to Have:
- Experience with Temporal workflow orchestration.
- Familiarity with AI-native integration patterns: MCP tool registration, scoring API contracts, LLM gateway usage.
- Working knowledge of Python for understanding AI/ML service outputs and integrating scoring results.
- Background in enterprise-scale SaaS platforms: eCommerce, supply chain, or similar.