Staff Backend Engineer

Role Overview

We are seeking an experienced Staff Backend Engineer to design, architect, and build scalable AI-native backend services. You will define hexagonal architecture patterns at automotive retail scale, owning service boundaries and driving the integration of advanced AI models into our core infrastructure.

Key Responsibilities:

• Architect Java (Spring Boot 3.x) microservices using strict hexagonal architecture to decouple domain cores from infrastructure adapters for Kafka, Elasticsearch, Redis/Aerospike, and various databases (MongoDB, PostgreSQL, Cosmos DB).
• Own core business logic end-to-end: design clean domain models, define port interfaces, manage production operations, and evolve event contracts via Kafka.
• Build high-performance REST APIs and robust event-driven pipelines that ingest and produce data across multiple source systems securely.
• Design optimized MongoDB schemas for document storage with advanced indexing strategies (TTL, multi-tenant isolation) to handle high-throughput workloads.
• Create efficient Cosmos DB models focusing on partition keys and consistency levels tailored for distributed state and event storage.
• Implement durable campaign execution workflows using Temporal orchestration frameworks.
• Pioneer AI-native integration patterns including MCP tool registration, scoring model serving contracts, LLM gateway security, and agentic workflow design.
• Analyze system performance to resolve bottlenecks in services, data pipelines, and search engines (Elasticsearch).
• Ensure all systems adhere to industry standards for reliability, scalability, and comprehensive observability.
• Mentor engineering teams across all levels to establish a world-class development culture.

Technical Requirements:

• +8 years of production backend experience with Java 21/Spring Boot 3.x in microservices environments.
• Deep expertise in Hexagonal Architecture (Ports & Adapters) to ensure clean separation of concerns and independent testing capabilities.
• Advanced proficiency with Kafka: consumer groups, partitioning strategies, offset management, idempotency guarantees, and event sourcing patterns.
• Mastery of MongoDB schema design for high-scale document stores including aggregation pipelines and tenant isolation techniques.
• Strong PostgreSQL skills in complex query optimization, migration strategy, and relational data modeling.
• Experience with globally distributed key-value stores like Cosmos DB: partitioning strategies, consistency tuning (CAP theorem), and read/write throughput patterns.
• Solid background in Elasticsearch for index management, relevance tuning, and search performance scaling.
• Broad experience with caching layers (Redis/Aerospike) for session state and high-speed data retrieval on AWS infrastructure (EKS, MSK).

Preferred Qualifications:

• Hands-on experience orchestrating durable workflows using Temporal.io.
• Familiarity with Model Context Protocol (MCP) tool registration patterns for AI agent integration.
• Working knowledge of Python to support ML pipelines, feature serving, and LLM gateway implementations alongside Java systems.