Top Artificial Intelligence Jobs in Poland

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Design, build, and operationalize AI agentic solutions across the software lifecycle. Own backend responsibilities, contribute to frontend, define technical requirements, deliver maintainable code, collaborate with clients, and stay current with AI, LLM orchestration, and evaluation practices.

Leads the architecture, development, and production deployment of scalable AI and generative AI solutions. Designs LLM, RAG, multimodal, and agentic systems; translates business needs into technical roadmaps; guides multidisciplinary teams; establishes MLOps, governance, security, monitoring, and responsible AI practices; mentors technical staff; and advises stakeholders on AI strategy, feasibility, and adoption.

Designs and deploys production-grade generative AI, RAG, and agentic systems for enterprise clients. The role combines client workflow discovery, architecture, production coding, cloud-native AWS delivery, evaluation and monitoring, cost optimization, technical scoping, stakeholder communication, and knowledge transfer. Candidates must independently solve ambiguous problems, engage executives and engineers, and own technical outcomes across industries including financial services, insurance, healthcare, and life sciences.

Build and deploy production-grade generative AI and agentic systems for enterprise clients. Responsibilities include developing RAG applications, evaluation harnesses, backend services, data pipelines, and cloud-native AWS deployments; implementing LLMOps and AgentOps practices; monitoring quality, drift, cost, and latency; integrating APIs; improving reliability; contributing to architecture and reusable blueprints; and mentoring AI engineers in a forward-deployed consulting environment.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Build and deploy production generative AI, RAG, and agentic systems for enterprise clients. Translate business workflows into technical solutions, develop evaluation harnesses, write production code, and deliver cloud-native systems on AWS. Lead architecture reviews, technical proposals, scoping, monitoring, cost optimization, and client handoff. Partner with senior stakeholders, own technical direction, mentor engineers, and help evolve reusable AI Blueprints across financial services, insurance, healthcare, and life sciences.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

Design, build, and operate Redpanda’s Agentic Data Plane and cloud services across AWS, Azure, and GCP. Lead architecture and technical projects, develop scalable and reliable systems, automate infrastructure provisioning and self-healing, and ensure resilience, observability, security, and zero-downtime upgrades. Participate in on-call escalation, collaborate across engineering and customer-facing teams, and help achieve a 99.99% SLA.

Designs and delivers production-grade GenAI and agentic AI solutions for enterprise clients. Leads discovery, solution architecture, rapid prototyping, technical presales, evaluation, integration, and MVP or first-production delivery. Works with LLMs, enterprise data, workflows, orchestration, vector databases, cloud AI services, and tool-calling systems. Translates ambiguous business problems into validated solutions while addressing cost, security, compliance, observability, responsible AI, adoption, and production readiness.

Design, automate, secure, and operate AWS and Azure cloud infrastructure supporting AI and generative AI platforms. Build Terraform-based infrastructure, manage networking, identity, containers, Kubernetes, monitoring, governance, and cost controls. Support production operations, incident response, troubleshooting, documentation, and compliance in partnership with AI, Data, Security, and Application teams.

Design, develop, deploy, and operationalize Generative AI and LLM solutions for supply chain operations. Build RAG pipelines, prompt orchestration, agent workflows, Python services, and Azure AI architectures. Establish evaluation, testing, monitoring, governance, and deployment practices while ensuring reliability, scalability, security, and adoption. Collaborate with stakeholders and business users to validate use cases, coordinate testing, gather feedback, and enable low-code practitioners and subject matter experts.

Own and improve AI-powered analytics capabilities, including Talk2Data, evaluation frameworks, data quality validation, and AI output governance. Translate business needs into analytical requirements, collaborate with data and product stakeholders, support cloud data platforms, lead AI proofs of concept, diagnose tooling integrations, and drive organizational adoption through enablement sessions and documentation.

Build and scale production-grade generative AI systems, including LLM pipelines, RAG systems, agentic workflows, MCP servers, and APIs. Measure quality, latency, and reliability using LLMOps and MLOps practices, automated evaluations, observability, and benchmarking. Collaborate with engineering, product, and data teams while maintaining strong code quality, testing, documentation, monitoring, and security standards for AI capabilities used in a global fintech trading platform.

Lead full-cycle recruiting across Poland and global markets, partnering with hiring managers to define roles, develop sourcing strategies, manage requisitions, assess candidates, and guide offers through onboarding handoff. Use recruiting data and market insights to improve hiring quality and efficiency, support inclusive interviews, strengthen candidate experience and employer branding, and contribute to talent acquisition programs and process improvements.

Build and productionize AI solutions for customs operations, including document understanding, classification, retrieval-augmented generation, structured extraction, and multi-step agent workflows. Own evaluation, testing, monitoring, cost and latency optimization, pilot implementation, and production performance. Collaborate with operations, IT, transformation, legal, security, and technology partners while supporting privacy, security, and EU AI Act compliance.

Build and improve Restream’s web interfaces and user-facing features for the AI Clips team. Design UI and business logic, collaborate with product designers, participate in code reviews, improve system reliability, and evaluate emerging technologies. The role requires strong JavaScript and TypeScript expertise, advanced React knowledge, CSS preprocessor experience, and familiarity with backend development, cloud infrastructure, containers, and continuous deployment.

Develop and serve AI platforms, improve AI functionality, solve ambiguous technical problems, and collaborate with research, engineering, business, and product teams. The role involves production-quality backend development, API and SDK construction, software engineering best practices, mentoring, leadership, and potentially deploying machine learning models through MLOps.

Design and deliver AI platform features and agents for a PLM application: build RAG/vector search knowledge systems, conversational AI, cloud-native deployments, CI/CD/GitOps pipelines, and ensure scalable, observable production delivery while partnering with product through discovery and iterative delivery.

As a Senior Forward Deployed Engineer, you will collaborate with customers to develop AI-driven solutions, integrating various technologies while ensuring performance and reliability outcomes, focusing on autonomous agents and workflow automation.

Create high-volume, high-quality marketing content using generative AI and AI agents. Produce video, image, and social content, develop attention-driving campaigns, track AI and culture trends, and iterate quickly with the marketing and founding team.

Build features, prototypes, and demos using modern AI coding tools and agents; iterate prototypes into reliable, scalable production; direct and evaluate AI coding agents; collaborate closely with founders and senior engineers; explore and improve AI development workflows to accelerate product delivery.

Lead AI-native engineering: build and ship production-grade features, convert AI-assisted prototypes into reliable systems, own architecture/security/quality, direct AI coding agents, define engineering standards, and work closely with founders on product and technical strategy.

The AI Engineer will build and maintain AI agents for performance media trading, integrate data, ensure system reliability, and implement evaluation frameworks.