• Develop and maintain RESTful APIs that facilitate the effective management of GPU clusters, virtual machines, and dedicated servers. • Utilize a modern technology stack including FastAPI, Python, OpenStack, Rest API, Asyncio, PostgreSQL, Docker, relational databases, RabbitMQ, Redis, and Kubernetes to build reliable and maintainable code. • Bridge the gap between development and operations, enhancing our CI/CD processes and infrastructure reliability through proactive service monitoring and problem resolution. • Take an active part in the lifecycle management of compute products from initial design to implementation, focusing on scalability and customer needs. • Design and implement system architectures that support high availability and disaster recovery principles. • Engage with team members across the organization to integrate development and operations objectives, facilitating a DevOps culture. • Drive innovation by staying current with industry trends and emerging technologies, applying this knowledge to improve internal processes and solutions.

• Develop and enhance our Django-based Billing Engine within a Kubernetes (K8s) cluster, focusing on high performance and reliability. • Participate in the software development lifecycle, focusing on architectural and system design, implementation, and maintenance. • Work closely with the product management, development teams, and QA to define feature specifications and deliver high-quality solutions. • Interface with an existing infrastructure of services, utilizing APIs and message brokers to facilitate seamless integration and data exchange. • Design and implement system architectures that support high availability and disaster recovery principles. • Participate in code review and share knowledge to improve code quality, software architecture, and user experience.

• Designing and implementing core components of Gcore’s cloud account management platform, including access control, audit logging, product availability, and organizational management features. • Writing high-quality Python code using FastAPI, relational databases, RabbitMQ, Redis, and other technologies to build secure, scalable, and reliable services. • Developing systems that manage identities, permissions, and account-level configurations across a wide range of Gcore products — including virtual instances, GPU clusters, inference platform, managed Kubernetes, object storage, and more. • Building APIs and internal tools to provide real-time visibility into account activities, user actions, and product-level availability. • Ensuring the performance, security, and observability of services through monitoring, optimization, and testing best practices. • Working closely with cross-functional engineering teams and product management to align on roadmap priorities and ensure seamless integration of account and access services across all Gcore platforms.

• Design and implement AI inference and model training cloud products optimized for Kubernetes - from autoscaling inference servers to distributed training jobs across GPU fleets • Write clean, efficient, and maintainable Go code to power Kubernetes controllers, operators, and custom resources supporting AI workloads • Build APIs, CLIs, and developer tools that simplify the deployment, lifecycle management, and monitoring of AI applications • Develop features that optimize serverless container workflows for AI, ensuring fast cold starts, resource-efficient scaling, and workload isolation • Contribute to system performance, reliability, and security, with a focus on AI-specific challenges such as GPU scheduling, job orchestration, and data throughput • Stay on top of Kubernetes ecosystem advancements (e.g., K8s-native ML tooling, scheduling improvements, SIGs) and influence our product roadmap accordingly