Top Artificial Intelligence Jobs in Krakow

Manage and deploy AI-driven automation and workflows, onboard clients, translate requirements into actionable automations, design prompts for LLMs, troubleshoot and iterate systems, coordinate with AI engineers, and track project timelines and performance.

Build, launch, and optimize high-performance outbound campaigns and automated GTM systems using Clay, Claude, SmartLead, HeyReach, n8n, and APIs. Translate buying signals into targeted prospecting workflows, iterate on messaging and sequences, track CRM attribution, and optimize for pipeline outcomes and campaign KPIs (launch velocity, conversion, pipeline contribution).

Design, develop, and deploy AI/ML models and LLM-powered workflows (chatbots, copilots, automation). Integrate models with APIs and SaaS platforms, fine-tune prompts, work with structured/unstructured data, monitor performance, and collaborate with product and engineering teams to deliver scalable AI solutions.

As an AI Engineer, you'll design AI tools for authors, enhance internal workflows, and build tools for AI development, collaborating with teams to identify AI opportunities.

Design and build workflows using node-based AI systems for video and image generation. Collaborate with teams to enhance AI-driven processes and create demos for various stakeholders.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Build and scale core systems powering GenAI customer agents. Design reliable, high-performance distributed services, work across Java/Scala/Python stacks, use AI dev tools to accelerate engineering, ensure strong tests and observability, collaborate on architecture and product decisions, and deliver customer-focused solutions.

As an AI Creative Producer, you'll develop and scale AI-generated content across various platforms, collaborate with teams, and enhance production workflows.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Design, build, and maintain Claude-powered AI agents and modular multi-agent systems to automate bookkeeping and accounting workflows. Integrate with accounting platforms (Xero, QuickBooks, MYOB, etc.), implement human-in-the-loop processes, confidence scoring, audit trails, and continuous learning mechanisms, and collaborate with leadership to prototype and scale AI-driven accounting platform capabilities.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

The role requires reviewing and evaluating AI-generated outputs and original content while applying local insights and maintaining quality standards. Strong analytical skills and attention to detail are necessary.

Review, evaluate, and annotate AI-generated text, images, audio, and video for accuracy and guideline compliance. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve datasets and annotation workflows.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Lead end-to-end development and integration of AI features (LLMs/ML) into web/mobile apps and APIs. Design AI-first UX, craft and optimize system prompts, build agentic workflows (LangChain/CrewAI/AutoGen), implement error-handling and hybrid cloud/on-prem deployments, and collaborate within product squads using Agile/Scrum.

Design and implement CI/CD and IaC for LLMs, automate model and dataset versioning, provision GPU/TPU infrastructure, enable safe experiment gates and progressive delivery, and build monitoring/observability and feedback loops for production LLM endpoints.

Lead design, build, and operate a multi-tenant AI platform and LLMOps infrastructure across cloud and on-prem. Own vector DBs, feature stores, model hosting (Kubernetes + GPU orchestration), and developer self-service tooling. Standardize LLM-as-a-service offerings and enable squads with APIs, templates, and documentation.

Produce high-performing AI-driven paid ads from concept to final cut using AI avatars, voiceovers, b-roll, and motion graphics. Match and iterate on existing ad styles, collaborate with copywriters, optimize for hooks/pacing/CTAs, scale volume, and stay current with AI tools to innovate creative formats.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.

Perform quality checks on filters and options in desktop and mobile views, ensuring relevance to customer queries and preferences. Follow guidelines, conduct online research, review assessments for accuracy, and complete required training and qualification tests before starting.