DevOps Engineer
Team: Federated Data Lake
Project: BRAIN++ – Bulgaria’s AI Factory Location: Sofia Tech Park (Hybrid / On-site) Type: Full-time
1. About the Project and Our Mission
As part of BRAIN++, Bulgaria’s first comprehensive AI Factory, we are building the infrastructure required to support the development, adaptation, and deployment of national-scale foundation models. The initiative aims to strengthen Bulgaria’s AI ecosystem while contributing to European digital sovereignty through the creation of advanced AI technologies, governance frameworks, and cross-sector AI applications.
Within this initiative, the Federated Data Lake team is building the backbone of a highly secure agentic AI ecosystem: a Federated Data Lake designed to securely transport, store, and process massive AI training datasets across institutions.
The infrastructure enables trusted collaboration between research organizations, public institutions, and industry partners while maintaining strict compliance with European data governance frameworks and the EU AI Act.
To support this ecosystem, we are building a high-performance distributed storage layer and an encrypted mesh network architecture that allows autonomous AI agents and training pipelines to securely access massive dataset archives. These systems must guarantee high throughput, secure communication, and reliable dataset streaming directly into AI training workflows.
We are looking for engineers capable of designing ultra-fast distributed infrastructure from the ground up. We do not measure candidates by the number of years spent in a role; we care about the ability to build secure, high-performance data systems capable of supporting large-scale AI development.
2. Role Overview
We are seeking a DevOps Engineer to design and operate the core data infrastructure powering the Federated Data Lake ecosystem.
This role focuses on building the secure networking, distributed storage architecture, and high-performance data pipelines required for large-scale AI training and agent-driven workflows.
You will be responsible for architecting the encrypted mesh networks and distributed blob storage systems that allow AI agents and training pipelines to securely retrieve massive datasets across federated environments.
The role requires a multidisciplinary systems engineer capable of combining deep knowledge of network engineering, distributed storage systems, and backend infrastructure to support a large-scale AI ecosystem.
3. Required Qualifications
Secure Overlay Networking
Elite, proven experience designing and deploying scalable encrypted peer-to-peer mesh networks. You must possess a strong architectural understanding of overlay networking systems such as Slack’s Nebula or similar protocols to enable secure, flat-network communication between federated nodes and autonomous AI agents.
High-Throughput Archive Storage
Deep architectural mastery of distributed blob and object storage systems designed for extremely large files, such as dataset archives or tarballs. While we utilize SeaweedFS, we care more about your fundamental understanding of high-bandwidth sequential I/O, large-file chunking, and distributed storage tuning.
You must be capable of designing storage layers that guarantee fast, reliable, and uninterrupted retrieval of massive datasets directly into AI training pipelines.
Federated Architecture & Security
Exceptional problem-solving capability in designing federated data access architectures across distributed mesh networks. You must understand how to construct zero-trust data pipelines where dataset access is strictly governed by W3C Verifiable Credentials presented by autonomous AI agents.
Multidisciplinary Systems Engineering
You are fundamentally a backend and network engineer with deep knowledge of file system internals, network protocols, and distributed systems architecture. This includes practical understanding of TCP/UDP networking, NAT traversal, and distributed system reliability.
Zero-Tolerance Data Pipelines
A verifiable track record of securely transporting massive datasets across distributed systems without packet loss, corruption, or security vulnerabilities.
4. Preferred Qualifications
● Experience integrating distributed storage systems directly with AI training pipelines, such as streaming large dataset archives directly into PyTorch or TensorFlow training workflows.
● Deep understanding of cryptographic key exchange and Certificate Authority management within Nebula networks.
● Previous experience building data infrastructure integrated with permissioned distributed ledgers such as R3 Corda.
● Familiarity with Open Digital Rights Language (ODRL) or similar frameworks for dataset licensing and governance.
5. What We Offer
● Opportunity to work on BRAIN++, Bulgaria’s flagship initiative for building national AI infrastructure.
● Participation in designing and operating the core data infrastructure powering the Federated Data Lake and national AI foundation models.
● Work on cutting-edge distributed systems supporting agentic AI and large-scale machine learning workflows.
● Collaboration with multidisciplinary teams of AI engineers, distributed systems experts, and data scientists.
● Competitive salary aligned with the seniority of the role.
● Flexible working environment
● Opportunities for professional development and research collaboration
● Access to advanced computing infrastructure and large-scale AI datasets
Deadline: April 8, 2026
More information and application: [email protected]
Project No. 101250707 – BRAIN++ is implemented under a funding agreement with the European High-Performance Computing Joint Undertaking and the European Commission.
Sofia Tech Park is Bulgaria’s first science and technology park – a hub for researchers, start-ups, and established technology companies, supporting the development of innovation in the fields of ICT, life sciences, and clean energy.