• Design and develop software applications that incorporate AI and Machine Learning models • Integrate AI models with existing systems, APIs, data pipelines, and distributed architectures • Collaborate with Data Scientists and Data Engineers on building, training, versioning, and deploying models • Implement MLOps practices to ensure automation, monitoring, traceability, and scalability of models in production • Develop solutions based on LLMs (Large Language Models), such as chatbots, recommendation systems, classification, semantic search, and intelligent automation • Ensure code quality, security, performance, and software engineering best practices • Participate in architectural decisions, technical reviews, and the definition of standards • Drive continuous improvement of solutions by evaluating model and software performance metrics • Document technical solutions and share knowledge with the team

• Lead and facilitate weekly live online sessions using structured course materials. • Supervise Assistant Facilitators, ensuring consistent learner engagement and marking quality. • Provide 1-to-1 guidance and academic feedback to learners in line with defined rubrics. • Monitor and maintain high standards of delivery, assessment, and learner satisfaction. • Collaborate with internal academic and delivery teams to manage content updates and resolve challenges. • Participate in programme review discussions, sharing insights for continuous improvement. • Represent the programme in internal panels or forums as required. • Ensure all academic activity aligns with established frameworks and standards.

Role Description Are you a Senior AI 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 - Experience with Golang - 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 - 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 distributed data processing, Apache Spark, or Apache Beam - 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 - Work with advanced technologies - Contribute to mission-critical projects - Be part of a company recognized for excellence and innovation Personal Profile - 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

• AI/ML Development & Architecture • Design and develop scalable AI/ML solutions using LLMs and other machine learning models. • Architect and implement Generative AI systems that are scalable, resilient, and aligned with ethical AI practices. • Work as a Subject Matter Expert (SME) for Generative AI, partnering with Product and Engineering teams. • Develop and maintain end-to-end AI pipelines, including: Data preprocessing Feature engineering Model training and evaluation • Solution Design & Integration • Build extensible APIs and integrations to connect AI models with enterprise systems. • Develop low-code/no-code UI/UX solutions for rapid delivery cycles. • Design structured outputs (JSON, arrays, HTML) with nested nodes, ensuring seamless frontend/dashboard consumption. • Implement solutions integrating LLMs (e.g., GPT models) to extract insights and deliver actionable data. • Cloud & Distributed Systems • Architect and deploy solutions on cloud platforms such as AWS, Azure, or GCP. • Build and maintain distributed, scalable systems for AI workloads. • Optimize models and systems for performance, scalability, and efficiency. • AI Optimization & Prompt Engineering • Optimize generative AI models for improved accuracy and response quality.