Senior AI Backend Software Engineer

Our customer operates in the AdTech industry, delivering advanced technology solutions that optimize advertising performance and audience targeting. While the name is confidential, the organization is known for leveraging cutting-edge AI and data-driven strategies to enhance campaign effectiveness and deliver measurable business impact.

We are developing a next-generation AI-powered Knowledge Base and Gap Analysis platform for SysML-based engineering environments. The system enables large-scale engineering organizations to ingest, structure, analyze, and reason over complex MBSE artifacts and technical documentation. It supports both cloud and secure classified environments, improving traceability, identifying gaps, and enhancing decision-making in mission-critical projects.

You will join an international cross-functional team of engineers, AI specialists, and product experts collaborating across multiple locations to build scalable, high-impact solutions for complex engineering environments.

• Interview process that respects people and their time
• Professional and open IT community
• Internal meet-ups and resources for knowledge sharing
• Time for recovery and relaxation
• Bright online and offline events
• Opportunity to become part of our internal volunteer community

• Design and develop scalable AI-powered backend systems for SysML-based engineering environments
• Build and maintain distributed data ingestion and ETL pipelines for large-scale engineering artifacts and technical documentation
• Develop and optimize LLM-powered workflows for metadata extraction, semantic analysis, and entity resolution
• Implement AI agents and multi-agent orchestration workflows
• Design and improve RAG-based architectures and semantic retrieval pipelines
• Develop graph-based knowledge representation and traceability analysis solutions
• Work with graph databases, graph processing libraries, and semantic relationship modeling
• Build and optimize distributed data processing workflows using Apache Spark
• Collaborate with cross-functional engineering teams to integrate AI capabilities into platform services
• Design scalable and high-performance APIs and backend services
• Improve system reliability, scalability, observability, and performance across distributed environments
• Participate in architecture discussions and technical decision-making processes
• Contribute to cloud-native infrastructure and deployment workflows
• Support deployments in secure, air-gapped, or classified environments when required
• Create and maintain technical documentation and engineering best practices

• At least 4-5 years of commercial experience in software engineering, Data Engineering, or AI systems development

• Experience with Python

• Hands-on experience building distributed and scalable systems

• Practical experience with LLM-based applications and AI integrations

• Experience building AI agents and multi-agent systems

• Strong understanding of RAG architectures and semantic retrieval workflows

• Hands-on experience with graph technologies, graph libraries, or graph databases

• Strong understanding of ETL pipelines and large-scale data ingestion workflows

• Experience with cloud-native infrastructure and distributed environments

• Practical experience with backend platform development and API integrations

• Good understanding of semantic search, entity resolution, and metadata extraction

• Experience working with highly scalable and high-performance systems

• Strong problem-solving and communication skills

• Upper-Intermediate level of English

WILL BE A PLUS

• Background in Data Engineering

• Experience with at least one statically typed programming language like Java, Rust, Scala or Go.

• Experience with Apache Spark and distributed data processing

• Experience with Knowledge Graphs and graph-based semantic modeling

• Familiarity with MBSE or SysML environments

• Experience supporting air-gapped or classified environments

• Experience with vector databases and embedding pipelines

• Experience with Kubernetes and cloud platforms such as AWS, GCP, or Azure