Top Artificial Intelligence Jobs in Poland

Design, build, and operate an ML platform on GCP (Vertex AI). Develop and automate ML pipelines, provision infra with Terraform, create CI/CD workflows with GitHub Actions, build production Python APIs, implement model monitoring and observability, and support GPU-based training in HPC/Slurm environments.

Support the Enterprise AI PMO by maintaining the AI initiative portfolio, coordinating updates, managing governance and enquiries, preparing concise leadership reporting and dashboards, and improving reporting and operational processes using AI-enabled tools.

Design and lead scalable integrations connecting Moveworks and ServiceNow with enterprise SaaS. Define APIs, event-driven patterns, identity integrations, governance, and architecture standards to deliver AI-driven virtual assistant and automation solutions across large, multi-vendor environments.

Support the Enterprise AI PMO by maintaining the AI initiative portfolio, coordinating updates, managing governance requests, triaging enquiries, and preparing concise reports and dashboards for leadership to enable informed decision-making.

Lead strategy and delivery of enterprise AI and intelligent automation programs. Identify high-value use cases, design and deploy RPA and AI/ML solutions, build automation roadmaps and governance, drive cost savings and ROI, integrate automation into transformation programs, and mentor teams while advising senior stakeholders and clients.

Lead a team in supervising AI risk management for the financial sector, develop frameworks, coordinate with agencies, drive research on AI trends, and manage project deliverables.

Design and scale GTM systems and AI-powered workflows across sales, marketing, and product. Build agents, integrate Salesforce/HubSpot, turn product and user data into growth signals, create lead-scoring/routing/onboarding flows, identify bottlenecks, run experiments with founders to drive measurable revenue impact.

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.

Record short video and/or audio clips and transcribe/listen to clips to capture tone, intent, and emotion for AI training. Work remotely with a quiet environment, 1080p camera, and strong vocal expression.

Design and supervise technical architectures for intelligent agents and LLMs, implement MCP and A2A multi-agent flows, integrate vector stores and RAG, collaborate with MLOps for continuous deployment, memory and context management, and work with product, UX, data, and backend teams to align solutions with business needs.

Lead design and delivery of enterprise AI and automation transformation initiatives. Oversee program execution, stakeholder alignment, value realization, and adoption of intelligent automation frameworks across regions.

Lead business transformation initiatives focusing on AI and automation. Manage projects, optimize delivery using methodologies, and ensure stakeholder alignment.

Lead technical design and delivery of Data & AI systems: architect data pipelines, RAG and agent integrations, build FastAPI backend services in event-driven environments, use AI-assisted coding, mentor engineers, own production reliability, and coordinate cross-functional delivery from design through rollout and post-release operations.

Design, build, and productionize scalable ML solutions and platforms: convert research code to reusable Python packages, implement experiment tracking and versioning, create distributed training and deployment pipelines, enable monitoring and automated retraining, enforce ML lifecycle governance, and collaborate with cross-functional teams to deliver secure, production-ready ML systems.

Build and operate a composable, event-driven internal platform: design and ship backend services (Python/Go), frontend admin tools (TypeScript/React), integrations with SaaS and cloud APIs, BPMN/workflow orchestration, and LLM/AI tooling across the development lifecycle while owning production reliability, security, and observability.

Build and operate internal, event-driven platform services end-to-end: backend services (Python/Go) on GCP Cloud Run, TypeScript/React UIs, integrations with third-party SaaS (REST, webhooks, OAuth), BPMN workflow orchestration, and apply AI tooling across the development lifecycle while ensuring security, observability, and production reliability.

Build and operate internal, event-driven platform services and UIs across the stack. Integrate third-party SaaS via APIs/webhooks/OAuth, implement observable BPMN workflow orchestration, use AI tooling throughout the SDLC, review AI-generated code, and ensure production reliability, security, and cost control.

Build and operate internal, event-driven platform services and UI features end-to-end. Integrate third-party SaaS and cloud APIs, implement observable BPMN workflow orchestration, use AI tooling across the SDLC, ensure security and reliability in production, and contribute engineering standards and playbooks.

Build and deploy AI-native systems for BJAK's neobank: AI agents, RAG/retrieval systems, tool-calling and automations for onboarding, KYC, support, risk, CRM and operations; work with product and engineering to ship reliable, evaluated, low-latency, cost- and compliance-aware AI-enabled workflows.

Build and maintain backend services, APIs and workflows for an AI-driven neobank app. Integrate with banks, payment gateways, insurers and partners; design account, transaction, reconciliation, notification and operational systems; improve reliability, observability, security and performance; collaborate with product and mobile teams to ship customer-facing journeys for regulated financial products.

Build end-to-end customer-facing and internal features for an AI neobank: web flows, backend APIs, admin tools, dashboards, integrations and production reliability. Collaborate with product, design, mobile and ops to ship quickly and iterate using analytics and feedback.

Build and productionize AI-native systems for a neobank app: create AI-powered workflows, agents, and automations across customer support, onboarding, claims, payments and operations; integrate LLMs with internal data, APIs and knowledge bases; design evaluation, monitoring and fallback flows; prototype, test with users, and ship robust, reliable systems with attention to privacy, cost and failure modes.

The AI Content Expert II creates and annotates complex training data for LLMs, ensuring quality and producing text across various styles. Responsibilities include auditing work, making independent decisions, and collaborating with scientists and engineers for data quality improvement.

The role involves creating and annotating training data for Large Language Models, conducting audits, and ensuring data quality, alongside collaboration with scientists and engineers.

Lead the vision and execution for AI analytics in JetBrains Console, focusing on customer adoption, usage, cost, and decision-making.