Backend Engineer

We are looking for strong Backend Engineers with practical experience integrating applications, services, and enterprise systems. You should be comfortable building APIs, connecting systems, handling data and asynchronous workflows, and taking changes through testing and deployment. We welcome different technology backgrounds and value engineering fundamentals, ownership, and learning ability.

Responsibilities:

• Design, build, and maintain backend services, APIs, and business logic.
• Integrate internal applications, third-party platforms, and enterprise systems through APIs, webhooks, messaging, and background jobs.
• Build reusable connectors and adapters with authentication, data mapping, validation, and error handling.
• Implement reliable workflows with timeouts, retries, idempotency, failure recovery, and reconciliation.
• Work with databases, caching, application permissions, and asynchronous processing.
• Understand existing codebases, trace dependencies, and make reliable changes across services.
• Write automated tests, verify API contracts, and troubleshoot failures across connected systems.
• Contribute to containerization, CI/CD pipelines, AWS deployments, and production monitoring.
• Use AI-assisted development tools to accelerate delivery while remaining accountable for correctness and quality.

Requirements:

• Experience: Typically 3-10 years; strong candidates outside this range are welcome.
• Backend programming: Strong skills in at least one language, such as Python, Java, TypeScript/JavaScript, or Go, with hands-on experience developing backend services and REST APIs.
• Integration engineering: Experience connecting applications or systems, including authentication, request/response mapping, validation, and handling integration failures.
• Object-oriented programming: Good working understanding of abstraction, encapsulation, interfaces, and composition, and the ability to structure modular, maintainable code.
• API fundamentals: Understanding of HTTP, JSON, API contracts, status codes, pagination, and versioning.
• Databases: Practical experience with SQL, relational databases, schema design, and transactions.
• Security: Understanding of authentication, authorization, tokens, service credentials, and application permissions.
• Engineering practices: Experience with Git, code reviews, automated testing, and systematic debugging, along with exposure to CI/CD and application deployment.
• Agentic AI and MCP familiarity: Basic understanding of agents, tool calling, and Model Context Protocol, including how agents interact with APIs and backend systems. Exposure through professional work, personal projects, or experimentation is welcome.
• Ownership and judgment: Ability to navigate unfamiliar codebases, investigate issues across service boundaries, communicate clearly, and make timely decisions.

Useful experience: you do not need all of these:

• Backend frameworks and runtimes: FastAPI, Django, Node.js, or equivalents.
• Enterprise integrations: CRM, ERP, identity providers, billing, order management, inventory systems, or other business platforms.
• Integration patterns: Webhooks, event-driven processing, scheduled synchronization, batch integrations, and workflow orchestration.
• Messaging: Kafka, RabbitMQ, SQS, SNS, EventBridge, or equivalent technologies.
• Data: PostgreSQL, MySQL, Redis, DynamoDB, query optimization, and database migrations.
• AWS: ECS/Fargate, Lambda, API Gateway, S3, RDS, IAM, Secrets Manager, and CloudWatch. Relevant Azure or GCP experience is also welcome.
• Containers and orchestration: Working understanding of Docker and Kubernetes, including containerizing applications, configuration, and deployment troubleshooting.
• Testing tools and practices: Familiarity with Robot Framework, Playwright and BDD scenarios written in Gherkin. Experience with API, integration, and end-to-end testing across connected services.
• GitLab and CI/CD: Familiarity with GitLab repositories, merge requests, and pipelines for automated builds, tests, and deployments. Experience with GitHub Actions, Jenkins, or similar tools is also relevant.
• Infrastructure as code: Basic understanding of Terraform, including reading and modifying existing configurations and reviewing infrastructure plans.
• Security and reliability: OAuth2/OIDC, JWT validation, RBAC, least-privilege access, audit logging, monitoring, tracing, and production troubleshooting.
• AI integrations: Model APIs, MCP servers/clients, agent frameworks, and workflows.
• AI-assisted/agentic coding: Familiarity with coding assistants or coding agents for implementation, testing, debugging, or exploring codebases is helpful, but not mandatory Candidates should be willing to learn these tools and critically review and validate generated code.