Mid-Level Backend Engineer (Kotlin) – AI & Cloud-Native

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We are looking for a Mid-Level Back-End Engineer to work at the intersection of traditional back-end development, cloud-native infrastructure, and Artificial Intelligence within an enterprise monitoring and observability platform. In this role, you will be responsible for designing and building scalable services using Kotlin and Spring Boot, modeling data in PostgreSQL, and containerizing applications for orchestration in Kubernetes. Beyond ensuring structural stability, you will directly integrate AI capabilities (such as OpenAI and Anthropic LLMs) into the product, processing high volumes of telemetry data to automatically suggest root causes and summarize metrics in a secure and reliable manner.

What you will do:

• Design and implement high-performance back-end services and REST APIs using Kotlin and Spring Boot.
• Model and optimize relational databases using PostgreSQL, handling schema migrations and complex queries for high volumes of telemetry data.
• Integrate Artificial Intelligence capabilities into the product by connecting the back-end to LLM APIs (OpenAI, Anthropic, or cloud-managed AI services).
• Structure the secure and predictable consumption of AI by preparing data, designing prompts, and utilizing structured outputs (function calling and JSON).
• Containerize applications and configure services to run reliably on Docker and Kubernetes, collaborating on Helm charts and deployment workflows.
• Contribute to system design discussions, helping define the architecture and the integration of cloud-native and AI capabilities within the platform.
• Ensure technical excellence by writing clean, testable, and maintainable code, while actively participating in code review routines.
  
  

What we expect from you:

• Solid software development experience using Kotlin and the Spring Boot framework (prior experience with Java is a plus).
• Strong command of REST API design and architectural best practices.
• Hands-on experience with PostgreSQL, including relational schema design, querying, and performance optimization.
• Practical exposure to consuming LLM APIs (even in initial projects), understanding how to handle responses, structure prompts, and manage JSON-formatted outputs.
• Working knowledge of Docker (ability to build, run, and debug containers) and familiarity with essential Kubernetes concepts (Pods, Deployments, Services, and ConfigMaps).
• Familiarity with version control using Git and executing CI/CD pipelines and workflows.
• Strong problem-solving skills and an analytical profile to navigate complex systemic environments.
• Good written and spoken English communication skills to ensure seamless alignment with global teams.
  
  

Nice to Have:

• Practical knowledge of Retrieval-Augmented Generation (RAG) architectures or embedding-based search.
• Familiarity with the OpenShift environment and its security constraints (such as running non-root containers).
• Applied knowledge of enterprise observability tools and standards (Prometheus, Grafana, OpenTelemetry).
• Previous experience with stream processing tools, such as Apache Flink or Kafka.
• Deep understanding of system telemetry, with the ability to diagnose anomalous patterns (e.g., CPU spikes, HTTP 500 error bursts, connection-pool timeouts).
  
  

If you’re ready to take your engineering career to the next level, we’d love to hear from you!

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