Top Artificial Intelligence Jobs in Czechia

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Architect, develop, and deploy AI-enabled solutions using React frontends and Python or .NET backends. Enhance LLM engines with Azure OpenAI, sync data to Azure AI Search, use Kafka for messaging, build robust APIs, and mentor junior developers while collaborating with product, DevOps, and QA teams.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

Lead design and governance of ICON's end-to-end AI platform, multi-agent frameworks, and supporting services. Define cloud-native, scalable architectures, integration patterns, APIs, and standards; lead solution architecture, design reviews, and mentor engineering teams to deliver secure, high-performing AI/ML production systems.

Manage end-to-end release lifecycle for a multi-agent AI platform: own release calendar, plan and orchestrate feature/patch/hotfix deployments, coordinate cross-functional teams, enforce quality gates and compliance, monitor release performance, run post-release reviews, mitigate risks, and lead release communications and escalations.

Lead QA for AI platforms by defining test strategy, automation, and quality governance. Design AI-specific and CI/CD-integrated test frameworks, validate agent behavior, manage QA team, enforce quality gates, drive metrics and continuous improvement across engineering and product teams.

Lead development and operationalisation of Responsible AI frameworks and compliance processes. Translate regulations (EU AI Act, GDPR) into controls, implement risk assessment, bias detection, model validation, embed governance in development lifecycles, manage audits and incident response, and drive stakeholder engagement and training.

Lead Full Stack Engineer owns design-to-production of an AI-first repossession and remarketing platform. Shape architecture, build scalable resilient services, integrate LLMs/GenAI for validation and document processing, drive technical decisions, reduce technical debt, operate production systems, and mentor engineers while working in cross-functional agile teams.

Support Shopify administration and Flow automations, maintain Google Apps Scripts and API integrations, manage access and SaaS subscriptions, configure Recharge subscription flows, and document processes. Use AI tools (Claude, ChatGPT) and n8n to build automations, test end-to-end, and liaise with vendors and internal teams to keep e-commerce systems running smoothly.

Founding AI Engineer responsible for end-to-end R&D and deployment of AI systems (LLMs, diffusion, vision), building rapid prototypes, orchestrating multi-model agentic workflows, integrating diverse APIs, and shipping scalable web applications to support e-commerce and future SaaS products.

The role involves defining system architecture, conducting technical spikes, and designing agent behaviors in the development of a new LLM-based agent system.

The Head of Performance will lead performance marketing strategies, focusing on optimizing paid acquisition channels and utilizing AI tools for data-driven decisions.

Lead the AI Enablement programme at FNZ to accelerate Generative AI adoption, ensuring secure and compliant enterprise-wide change, while managing multiple workstreams and stakeholders.

As a Principal AI Researcher, you will lead AI research projects, mentor teams, and implement innovative AI technologies for Veracode's products.

Design, build, test, and maintain ML/AI software solutions; ensure product and technical excellence; apply ML, NLP, and large-scale system practices; collaborate across global teams; follow engineering best practices and support strategic goals.

Lead go-to-market planning for JetBrains AI Ecosystem launches, create clear messaging and launch assets, coordinate stakeholders, scale campaigns, gather market and customer insights, and track campaign performance to improve processes.

As an AI Solution Engineer, you will lead customer engagements by creating tailored AI and analytics solutions while collaborating with sales and technical teams to validate and implement these solutions.

The AI Engineer will develop AI solutions using LLMs, assist with agentic workflows, and collaborate on AI-powered features for packaging processes.

Create and edit short-form vertical videos for social media using AI tools, ensuring quality, character consistency, and appropriate syncing of audio elements.

The AI Engineer will design, build, and deploy AI solutions, optimize performance, manage pipelines, and collaborate with stakeholders on AI product development.

Lead evaluation and benchmarking of AI-driven genomic interpretation: own gene/disease/variant mapping logic, define accuracy and release criteria, establish regression standards, partner on automated validation, and guide where deterministic logic supplements models.

Design, implement, and run automated benchmarking and validation pipelines for AI-driven genomic interpretation. Expand regression datasets, execute large-scale validation across curated VCFs, surface accuracy regressions, build automated QA for AI releases, and integrate validation into CI/CD with dashboards and documented failure modes.

Lead a team of senior developers and oversee the development of scalable full-stack features, architecture, and API integrations. Mentor developers and establish best practices for engineering excellence.

Design, build, evaluate, and deploy LLM-driven features and AI systems. Collaborate with engineering and product teams to implement RAG, monitoring, CI/CD, and scalable cloud ML deployments while continuously improving model performance.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.