Role Description The client is seeking a Sr. AI Engineer/Sr. Machine Learning Engineer to design, build, deploy, and maintain production-grade AI systems across their data intelligence platform. This role will lead the development of LLM-powered applications, agentic workflows, retrieval-augmented generation systems, model evaluation pipelines, and scalable AI services. The ideal candidate combines strong machine learning expertise with practical production engineering experience. This person will own complex technical work from concept through deployment, mentor other engineers, and help define best practices for building reliable, observable, and secure AI systems. Essential Responsibilities - Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities. - Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines. - Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources. - Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods. - Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost. - Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review. - Translate prototypes and research concepts into reliable product features that can scale in production. - Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs. - Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices. - Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation. Qualifications - 5+ years of experience in machine learning engineering, AI engineering, data science engineering, or a related technical role. - 2+ years of experience building or shipping production GenAI, LLM, or AI-powered systems. - Advanced Python programming skills and experience building maintainable production software. - Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, or similar ML frameworks. - Experience with LLM applications, RAG systems, embeddings, vector databases, prompt engineering, and model evaluation. - Experience deploying AI / ML services using Docker, Kubernetes, CI/CD workflows, APIs, and cloud-native infrastructure. - Strong understanding of classical machine learning, deep learning, NLP, information retrieval, and model validation. - Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders. - Experience mentoring engineers, reviewing technical designs, or leading complex AI engineering initiatives. Preferred Qualifications - Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field. - Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools. - Experience with model-serving platforms such as vLLM, BentoML, Triton, Ray Serve, or similar systems. - Familiarity with ML observability, experiment tracking, model monitoring, and prompt/version management tools. - Experience with graph-based retrieval, knowledge graphs, multimodal models, large-scale data processing, or security-focused data products. - Experience with infrastructure-as-code, workflow orchestration, model routing, caching, batching, or quantization. Special Skills or Experience Required - Proven experience building and deploying production GenAI systems, including LLM applications, agentic workflows, and RAG pipelines. - Advanced Python and ML framework experience, including PyTorch, TensorFlow, Hugging Face Transformers, or similar tools. - Experience with LLM fine-tuning, prompt engineering, embeddings, vector databases, semantic search, and model evaluation. - Strong production engineering skills, including Docker, Kubernetes, CI/CD, model serving, observability, latency optimization, and technical leadership. Success Measures Success in this role will be measured by the delivery of reliable AI capabilities, improved model quality, reduced latency and cost, stronger evaluation coverage, improved observability, and the successful mentorship of other engineers. The role should help increase the speed and confidence with which the company can move AI features from prototype to production.

• Diseñar y desarrollar agentes de IA y soluciones inteligentes para resolver desafíos empresariales • Apoyar la automatización de procesos internos y del cliente utilizando herramientas y tecnologías de IA • Explorar y experimentar con capacidades emergentes de AI (LLMs, copilotos, herramientas de automatización, etc.) para identificar casos de uso innovadores • Trabajar en estrecha colaboración con equipos de Data, AI y negocios para traducir necesidades en soluciones técnicas • Asistir en la construcción de prototipos, pruebas de concepto (PoC) y demostraciones • Contribuir a documentar soluciones, aprendizajes y mejores prácticas • Analizar oportunidades para mejorar la eficiencia y productividad a través de la automatización • Mantenerse actualizado con tendencias, herramientas y tecnologías de IA, aportando nuevas ideas al equipo

• As an AI Engineer (w/m/d), you take ownership of the architecture and operation of our AI agents. • Work closely with Data Scientists to turn AI prototypes into robust, reliable, and scalable production systems – and continuously evolve them. • Architect reliable AI systems. • Define metrics, run systematic evaluations, and conduct A/B testing. • Work closely with Data Scientists to bring prototypes into production. • Evaluate and apply the latest AI/ML technologies and practices.

Role Description Ciklum is looking for an Expert AI Engineer to join our team full-time in Ukraine. As an Expert AI Engineer, become a part of a cross-functional development team engineering experiences of tomorrow. - Embed into product teams and work 1:1 with senior engineers on real tasks - Co-develop and refine ways of using AI in everyday engineering workflows - Help teams adopt “agentic” ways of working through practical application, not just guidance - Start with one developer per team (phased rollout, not all teams at once) - Primarily focus on developers, with potential to expand support to QA, BA, and DevOps over time - Use and adapt to the approved internal toolset (e.g. Kiro, potentially Claude), ensuring compliance with TUI standards - Collaborate with internal AI/innovation teams to address tooling gaps or improvement opportunities - Engineers are actively using AI in their daily work in a meaningful way - AI is embedded into real development tasks (not just experimentation or training) - Teams become more efficient through practical AI adoption - Clear, reusable patterns for AI-supported development start to emerge 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 experience with AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face or equivalent) - Familiarity with both AI development tooling and backend/service-side technologies is beneficial - 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 - Proven experience in architecting and implementing end-to-end AI/ML solutions - Strong software engineering skills for AI system development, including data processing, API integration, and model serving - 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 - 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 Requirements - 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 - 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 cross-functional stakeholders, delivery teams, and clients - High degree of autonomy, accountability, and attention to detail in managing complex, multi-component AI systems 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 radical flexibility – work remotely or from an office, your choice - Care: We’ve got you covered with company-paid medical insurance, mental health support, and financial & legal consultations