Top Artificial Intelligence Jobs in Poland

Develop and optimize GPU- and CPU-accelerated numerical software libraries for AI, analytics, image processing, computer vision, and scientific simulation workflows. Responsibilities include designing scalable algorithms, performance tuning, benchmarking across architectures, collaborating on feature requirements and technical roadmaps, providing technical leadership, and improving library usability and performance.

Build scalable, resilient, and secure backend microservices and APIs for production AI applications across AWS and Azure. Integrate databases, third-party services, legacy platforms, and AI systems; optimize performance for real-time workloads; implement authentication, authorization, RBAC, and rate limiting; and apply testing, CI/CD, Git, code review, and containerization best practices.

Own the architecture and production delivery of a real-time voice AI copilot. Lead streaming speech-to-text, LLM extraction, Chrome extension integration, AWS infrastructure, latency optimization, evaluation, observability, cost control, concurrency, data isolation, monitoring, and rollback. Lead the AI engineering pod, manage client stakeholders and scope, present evidence-based results, deliver production epics, and transfer knowledge to client and internal teams.

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

Lead technical direction and architecture for AI-powered products, designing production-scale LLM workflows, RAG pipelines, and agentic features. Develop full-stack applications using Python or Node.js, AWS event-driven architecture, and React. Mentor engineers, promote AI-assisted development across the SDLC, collaborate with platform teams, manage technical risk, and ensure secure, auditable, compliant delivery for regulated financial services.

Design, build, and deploy production-grade autonomous agents, multi-agent systems, and LLM-powered enterprise applications. Develop reusable orchestration patterns, integrate models with APIs, databases, tools, and enterprise systems, and implement structured outputs, memory, monitoring, tracing, evaluation, and recovery workflows. Deploy secure, scalable AI solutions primarily on AWS while applying MLOps and AIOps practices. Collaborate with engineering, data, cloud, and business teams.

Build and secure cloud infrastructure for production AI solutions across AWS and Azure. Responsibilities include designing CI/CD pipelines, implementing Infrastructure as Code, integrating security scanning and compliance controls, managing Docker and Kubernetes environments, configuring IAM and network security, monitoring and logging systems, improving reliability, and supporting penetration testing remediation.

Embed with client teams to identify inefficient workflows, redesign decision flows around AI, and deploy production systems. Build agentic systems, RAG pipelines, or custom data pipelines as needed, while establishing telemetry, evaluation, and human-in-the-loop feedback. Manage stakeholders, translate business problems into executable technical plans, and enable client teams to operate and improve the systems after deployment.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Lead R&D projects to create mathematical models and numerical simulations for power grid reliability and stability with high renewable penetration. Develop, implement and validate simulation algorithms, collaborate globally, integrate optimization/ML methods, prototype solutions, publish results, and work with external partners.

Lead the technical vision and roadmap for cloud-native application development supporting mobile infrastructure services. Manage and mentor software engineers and architects, oversee scalable microservices architectures using containers and orchestration platforms, and direct the full software development lifecycle. Drive adoption of cloud technologies, DevOps, GitOps, AI-powered development tools, and low-code/no-code platforms. Collaborate cross-functionally, manage budgets and resources, ensure performance and reliability, and incorporate emerging technology trends.

Designs end-to-end UX solutions for mission-critical public safety systems while modernizing UX workflows with AI. Responsibilities include rapid prototyping, human-centered design, usability testing, human-in-the-loop interaction design, evaluating emerging AI tools, defining product visions, and guiding adoption of effective methods across the global UX organization.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Embed with customers to prototype, build, and ship production-grade AI and agentic applications; advise on AI strategy and deployment; codify repeatable architectures and playbooks; implement LLMOps/MLOps practices (RAG, fine-tuning, semantic search, observability); mentor teams and feed product improvements.

Lead and resolve complex incidents across large-scale NVIDIA GPU and Kubernetes infrastructure. Drive platform reliability, observability, automation, and operational standards. Mentor engineers, perform root cause analysis, collaborate with vendors and datacenter teams, and support AI-powered operational services.

Design and own the enterprise metadata and data discoverability architecture for a Databricks Lakehouse. Implement Active Metadata, Unity Catalog governance, lineage, tagging, and security; integrate metadata with BI and AI systems; evaluate Databricks features to automate metadata flows; provide technical leadership and mentor engineering teams.

Build, deploy, and productionize enterprise-grade AI solutions and agents using Snowflake AI Platform. Architect scalable ML pipelines, data models, ETL/ELT processes, and observability. Partner with customer data science and engineering teams to translate business needs into production systems, iterate rapidly, and ensure operational excellence and performance at scale.

Designs, reviews, and operates production-grade security automation and AI capabilities. Builds API integrations, agentic workflows, CI/CD pipelines, and orchestration systems supporting incident response, threat intelligence, detection engineering, and threat hunting. Establishes engineering, safety, reliability, auditability, and change-management standards while partnering with security teams. Evaluates AI and automation technologies, participates in on-call support, and enables scalable, maintainable security operations platforms.

Develop and optimise Salesforce Agentforce, AI-powered workflows, intelligent automation, and customer service solutions. Build scalable solutions using Apex, Lightning Web Components, Flows, SOQL, APIs, and Salesforce platform technologies. Support Data Cloud and external integrations, testing, monitoring, security, governance, and performance optimisation. Collaborate with architects, developers, technical leads, and business stakeholders to define AI use cases and deliver maintainable Salesforce solutions.

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead engineering excellence for Digital Assets and AI initiatives, ensuring architectural consistency, risk management, and continuous improvement in engineering practices.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.