AI Engineer

🕒 June 22

🇵🇱 Poland – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 AI Engineer

👻 Ghost score 20%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Ciklum

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2002

💼 Consulting

🏥 Healthcare

✈️ Travel

Consulting • Healthcare • Travel

Ciklum is a global digital engineering and AI-enabled product and platform services company that helps enterprises design, build, and scale AI-infused software, cloud, data, and automation solutions. It combines UX and product design with engineering, DevOps, data engineering, responsible AI, and edge/IoT capabilities to move pilots into production and deliver enterprise-ready outcomes across industries such as banking, retail, healthcare, hi-tech, automotive, and travel. Ciklum emphasizes platform-agnostic, scalable solutions—covering AI incubators, conversational AI, agentic automation, cloud and edge services, XR/AR/VR, and digital assurance—focused on transforming workflows and customer experiences for B2B enterprise clients.

📋 Description

• 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 • What Success Looks Like • 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

🎯 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 experience with AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face or equivalent), with awareness that production environments may also rely on Java and/or Node.js depending on the team’s technology stack • Familiarity with both AI development tooling and backend/service-side technologies is beneficial, as the role may span model development and integration into existing systems • 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 - 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) - AWS as primary • 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 • 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) • 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 flexibility – full remote working possibilities • Care: We’ve got you covered with company-paid medical insurance