• 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

• Design and development of AI solutions in Python. • Implementation of RAG (Retrieval-Augmented Generation) pipelines to improve model relevance. • Use of the AWS cloud, particularly AI/LLM services (e.g., Bedrock). • Integration of AI tools and orchestration platforms (N8N, OpenAI, Claude, etc.). • Experimentation and continuous improvement through prompt engineering and model fine-tuning. • Technology watch on AI innovations and proposing ideas/concepts to enrich our products. • Collaboration with product, data, and development teams to industrialize reliable and scalable AI solutions.

• Design, develop, and deploy AI-powered applications, agents, and automation solutions that improve business processes and customer experiences. • Build and maintain production-grade backend services, APIs, and integrations using Python and modern cloud technologies. • Develop workflow automations that support Sales, Revenue Operations, Customer Support, and other business functions. • Create and optimize data pipelines and integrations across enterprise platforms including Salesforce, Microsoft 365, Freshdesk, and other business systems. • Design and implement retrieval-augmented generation (RAG) architectures, LLM orchestration frameworks, and evaluation methodologies. • Lead the development of AI-powered simulation experiences, including conversational and voice-enabled learning solutions. • Identify opportunities to embed AI capabilities into existing BARBRI products and rapidly deliver high-impact features. • Partner closely with product, engineering, and business stakeholders to understand needs, define solutions, and deliver measurable outcomes. • Enable non-technical teams by providing guidance, training, and best practices for building and maintaining AI agents and automations.