Role Description Are you a Senior Backend Engineer passionate about building scalable AI-driven systems? Join us at Sigma Software to work on a cutting-edge platform that transforms how large-scale engineering organizations manage and analyze complex technical data. This is a remote position with flexible locations across Europe, Ukraine, and LATAM. You will be part of an innovative team leveraging AI, semantic graphs, and distributed processing to deliver impactful solutions. 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. - 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 Qualifications - At least 5 years of commercial experience in software engineering, Data Engineering, or AI systems development - Strong production experience with Go, Rust, or Scala - 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 - Experience with Apache Spark and distributed data processing - 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 Requirements - Background in Data Engineering - 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 Benefits - Analytical mindset with strong problem-solving skills - Ability to work independently and in a distributed team - Adaptability to secure and classified environments - Strong communication and collaboration abilities - Passion for AI-driven engineering solutions

• Understand business and product requirements and translate them into technical solutions • Design, develop, and maintain .NET applications • Contribute across the full software development lifecycle (SDLC) • Write clean, efficient, and maintainable code • Perform unit and integration testing to ensure code quality • Identify and fix bugs early in the development process • Support deployment to integration and production environments • Follow best practices for code merging, builds, and releases • Work closely with developers, architects, and product teams • Communicate progress, challenges, and solutions clearly • Stay up to date with new technologies and development practices

• Designing and delivering production-grade features end-to-end across Java (Spring Boot) backend and React frontend. • Driving full-stack architectural choices on a greenfield codebase - data models, API contracts, service boundaries, frontend state architecture. • Using AI agents and LLM-based tooling daily - for technical research, evaluating libraries and design trade-offs, generating and refactoring code, debugging, and writing tests. • Identifying repetitive parts of the development lifecycle (code review prep, documentation, ticket grooming, test scaffolding, release notes) and building AI-driven automations around them. • Leveraging LLMs and agentic workflows to rapidly digest technical documentation, RFCs, and vendor specs - turning hours of reading into actionable architectural input. • Working directly with business stakeholders - translating fuzzy requirements into clear technical scope, pushing back where it matters, and co-shaping the roadmap. • Operating in a Scrum cadence with the maturity to keep ceremonies useful and lightweight, not bureaucratic.

• Develop and maintain cross-platform modules using Kotlin Multiplatform Mobile (KMM) • Design and implement secure authentication and authorization flows (OAuth 2.0, OpenID Connect, MFA, SSO) • Integrate with identity providers and enforce Zero Trust principles (least privilege, continuous verification) • Collaborate with security architects to embed security best practices into mobile client design • Ensure secure storage of credentials and tokens using platform-specific secure storage mechanisms • Monitor and respond to runtime security signals (e.g., device integrity, jailbreak/root detection) • Write clean, maintainable, and testable Kotlin code for both shared and platform-specific modules