Role Description The AI Engineer is responsible for delivering value through the delivery of AI solutions to core business functions. This role focuses on applying industry AI techniques, tools, and platforms to develop scalable, production-ready systems that solve real business problems. - Own defined initiatives end-to-end, ensuring measurable impact. - Work closely with product, wider engineering teams, and business stakeholders. - Balance technical excellence with strong ways of working. - Contribute to a culture of accountability, trust, and continuous improvement. This role requires strong applied AI engineering skills across architecture, software engineering, and MLOps, combined with the ability to communicate effectively and drive business-aligned outcomes. Qualifications - Strong applied AI engineering skills. - Experience in architecture, software engineering, and MLOps. - Effective communication skills. - Ability to drive business-aligned outcomes. Company Description A global leader in applied safety science, UL Solutions (NYSE: ULS) transforms safety, security and sustainability challenges into opportunities for customers in more than 110 countries. - Delivers testing, inspection and certification services. - Offers software products and advisory offerings that support product innovation and business growth. - The UL Mark serves as a recognized symbol of trust in customers’ products. - Helps customers innovate, launch new products and services, navigate global markets and complex supply chains. - Supports sustainable and responsible growth into the future.

• This is a remote position. • FlexiSAF is a leading EdTech company driving innovation in education across Africa. • Participants will learn to design and build intelligent, LLM-powered applications using modern frameworks such as LangChain, Retrieval-Augmented Generation (RAG) and vector databases. • The program emphasizes practical, project-based learning; from implementing AI-driven workflows and chatbots to integrating models into production-ready applications. • Trainees will also gain exposure to Spring Boot for enterprise API integration and containerization for scalable deployments. • Upon successful completion, participants will be well-positioned to take on AI Engineering roles within any organization building intelligent products and systems.

• Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients • Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration • Work directly with client engineering and product teams to translate business needs into scoped, shippable technical solutions • Mentor and support other ML engineers on the team — code reviews, technical guidance, and knowledge sharing • Help shape internal best practices, tooling, and technical standards as the team grows • Represent TensorOps technically in client conversations, workshops, and (optionally) at industry conferences

Role Description Ciklum is looking for an Expert AI Engineer to join our team full-time in Poland. As an Expert AI Engineer, become a part of a cross-functional development team engineering experiences of tomorrow. Responsibilities: - Lead the design, development and deployment of advanced AI systems across Data Science and AI Engineering domains - Architect and implement scalable AI pipelines and LLM-driven applications, including retrieval-augmented generation (RAG), orchestration and multi-agent systems - Serve as the technical authority during client engagements, ensuring architecture quality, scalability, performance and reliability - Contribute hands-on to development activities, from experimentation and prototyping to production-grade implementation - Collaborate with cross-functional engineering, data, and product teams to align technical solutions with client and business objectives - Apply MLOps and LLMOps best practices for CI/CD, observability, evaluation and continuous improvement of AI models and pipelines - Integrate AI services with enterprise platforms (e.g., Confluence, Jira, GitHub, CRM, ERP) and ensure seamless interoperability - Drive innovation through internal frameworks and accelerators - Ensure all AI solutions comply with security, data privacy and responsible-AI standards - Mentor engineering peers and support knowledge sharing across teams and practices (e.g., contributing to Ciklum’s AI Academy) Commercial / Presales: - Act as technical SME in presales conversations, translating business challenges into viable AI engineering solutions - Partner with delivery and account teams to shape long-term client engagement strategies based on engineering excellence Internal resource management / team building and management: - Support recruitment efforts through technical assessments, interviews, and candidate evaluation - Mentor team members and contribute to cross-skilling initiatives within the AI and Engineering service lines - Uphold engineering excellence by promoting best practices, code quality, and delivery standards across projects Qualifications - 8+ years of professional experience in software, data, or AI engineering, including at least 3–4 years of hands-on experience designing and implementing AI/ML solutions - BSc, MSc, or PhD in Computer Science, Mathematics, Engineering, or a related quantitative field - Deep understanding of probability, statistics, and the mathematical foundations of machine learning and optimization - Proven experience building and deploying advanced AI systems, including Large Language Models (LLMs), multimodal, and generative AI architectures - Exposure to agentic system design, retrieval-augmented generation (RAG) and prompt engineering techniques - Strong proficiency in Python and common AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face or equivalent) - Solid understanding of modern AI engineering practices, including model lifecycle management, observability, evaluation, versioning and continuous improvement - Familiarity with AI solution delivery methodologies (e.g., CRISP-ML(Q), TDSP or modern agile ML lifecycles) - Ability to visualize, interpret, and communicate model outputs and insights effectively using modern tools and dashboards Requirements - Proven experience in architecting and implementing end-to-end AI/ML solutions — from data ingestion and model training to deployment, monitoring and optimization - Strong software engineering skills for AI system development, including data processing, API integration, and model serving (Python, SQL and optionally Java/Scala or similar) - Hands-on experience with cloud-native AI platforms and services (AWS SageMaker, Azure ML, GCP Vertex AI or NVIDIA AI stack) - Proficiency in designing scalable ML/LLM pipelines and applying MLOps/LLMOps best practices (CI/CD, orchestration, monitoring, versioning, and deployment automation) - Experience with diverse data modalities (structured, text, image, audio, video) and multimodal model integration - Familiarity with handling complex data scenarios such as class imbalance, time-series forecasting and anomaly detection - Understanding of security, data governance and compliance considerations in AI system design Benefits - Strong community: Work alongside top professionals in a friendly, open-door environment - Growth focus: Take on large-scale projects with a global impact and expand your expertise - Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications - Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies - Flexibility: Enjoy flexibility – full remote working possibilities - Care: We've got you covered with company-paid premium medical package through Luxmed - Benefits: Access the MyBenefit cafeteria platform, allowing you to choose perks that best suit your lifestyle and needs