Expert AI Engineer

Job Description Ciklum is looking for an Expert AI Engineer to join our team full-time in Slovakia. We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live. About The Role 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 Requirements We know that sometimes, you can’t tick every box. We would still love to hear from you if you think you’re a good fit! General Technical Requirements 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 Specific Technical 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 Domain Experience Broad exposure to enterprise-scale AI solution design across industries such as BFSI, Healthcare, Aerospace, Manufacturing, Energy, Telecom or Technology sectors Proven ability to translate business and operational requirements into robust AI system architectures that deliver measurable impact Familiarity with challenges of deploying AI in regulated environments and ensuring compliance with data privacy and protection frameworks (e.g., GDPR, CCPA, PCI DSS) Experience managing sensitive or high-value data (PII, PHI), implementing strong security, governance and access control mechanisms Understanding of enterprise data ecosystems and integration patterns (CRM, ERP, knowledge management or workflow systems) Business-related Requirements Proven experience delivering production-grade AI solutions that achieve measurable business and operational outcomes Strong ownership of the full AI engineering lifecycle — from problem framing and architecture design to deployment, optimization, and continuous improvement Ability to align technical decisions with business priorities, ensuring scalability, reliability, and measurable value from AI initiatives Excellent collaboration and communication skills to work effectively with