Senior Backend Engineer

Xperteez Technology is on the lookout for a Senior Backend Engineer to come on board as a contract contributor. The role offers a hybrid setup and sits in International City, AE, with a focus on building and testing cloud-based systems for AI applications. It’s a good fit for someone who enjoys solving tough backend problems and collaborating across disciplines to shape how AI models learn and operate, especially if you like working in a distributed, infrastructure-heavy space.

The team is exploring how to train next-generation AI systems, and this role centers on your backend prowess and systems design know-how. You don’t need prior AI experience to apply, the emphasis is on your depth in software engineering and how you approach complex, production-grade environments.

In this position, you’ll craft Reinforcement Learning Environments that challenge an AI model’s ability to design, deploy, troubleshoot, secure, scale, and recover cloud infrastructure. Expect to simulate realistic scenarios that cover distributed systems, networking, IAM, queues, durable storage, observability, rolling deployments, and disaster recovery, then build reproducible environments, deterministic validation tests, golden reference solutions, and intentionally defective variants.

Crafting cloud environments for AI evaluation

You’ll design and implement realistic cloud infrastructure setups that let AI models prove their competence in building and operating systems at scale. The work includes creating detailed, reproducible scenarios that exercise distributed components, networking, identity and access management, message queues, persistent storage, observability, rolling deployments, and disaster recovery. You’ll also develop deterministic validation tests and gold-standard reference solutions to ensure the environments are reliable, while producing defect scenarios to test how AI models respond under stress. Clear documentation of architecture, edge cases, and operational flows is essential so others can reuse what you’ve built. Collaboration with technical leads will help refine environment specs and acceptance criteria, and applying DevOps practices will yield scalable, secure, and maintainable evaluation tools for cloud-based systems.

What you’ll need to bring

Here’s what you’ll be expected to bring to the table, with extras that help but aren’t strictly required.

• Strong backend development experience across languages such as C++, Python, Rust, Go, Java, or JavaScript
• Solid DevOps know-how, including cloud infrastructure, CI/CD pipelines, and automation tools
• Proven ability to design, scale, and secure distributed production systems
• Deep understanding of networking, IAM, queues, durable storage, and disaster recovery concepts

Nice-to-haves include hands-on work building reinforcement learning testbeds, a track record of producing reproducible architectures and detailed documentation, and experience creating deterministic tests alongside golden references.

Pay, schedule and practical details

Compensation is output-based, with experts paid per task that meets project specifications. The time to complete tasks can vary depending on experience and workflow, and there are minimum submission requirements.Experts must commit to a minimum of 20 hours per week.

The role is hybrid and based in International City, AE, with the expectation to collaborate across environments and teams as needed. Start timing is quick, roles are typically filled within 48 hours, and if chosen, tasks would begin within 24–48 hours after onboarding.

Ways to stand out in this contract

Lead your resume with your strongest backend language capabilities and a proven DevOps track record in cloud infrastructure and automation. If you have experience spanning several of the listed languages, C++, Python, Rust, Go, Java, and JavaScript, make that breadth easy to spot at the top of your experience section.

Show, don't just tell, how you design, scale, and secure distributed systems. A concrete example or brief case study that highlights your approach to observability, IAM, queues, and disaster recovery will help you stand out, especially if you can point to reproducible environments you’ve built in the past.

Be ready to discuss how you would build deterministic tests and golden references for RL environments, including how you document architectural decisions and edge cases so others can reuse your work. In your interview, you’ll want to walk through an example of a test bed you’ve designed and the criteria you used to validate it.

When you’re preparing questions, ask about how success is measured for a given environment and how tasks are allocated across a 20-hour per week plan, so you can understand the pacing and expectations from the Hiring Manager and recruiters.

How to apply and what happens next

• Submit your application and complete the screening questions, so the team can start your review.
• Take part in the AI interview, which is approximately 30 minutes and will be reviewed by recruiters.
• If you pass the initial review, the Hiring Manager will evaluate and decide on next steps.