Top Artificial Intelligence Jobs in Poland

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead product direction and execution for major areas of an AI-enabled neobank app. Define roadmaps, priorities, standards, and success metrics across multiple squads; mentor PMs and guide designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement while focusing on user value and measurable outcomes.

Own product areas in BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define user problems, roadmaps, success metrics, and launch plans. Work cross-functionally with engineering, design, data, operations, compliance and business teams. Use data, research and feedback to iterate, simplify complex financial journeys, monitor performance, and balance UX, speed, business impact and compliance.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Lead development and evaluation of speech language models and automated scoring systems for learner and hiring applications. Build, fine-tune, and monitor acoustic, ASR, speech-to-speech, and LLM-based models; design experiments and validity analyses; perform error analysis and rater reliability studies; integrate with product and UX; apply responsible AI practices and support production monitoring and governance for high-stakes assessments.

Lead R&D and implementation of ML and statistical learning capabilities for English language learning products. Design, build, and monitor production-ready models (NLP, speech, psychometrics), document and present results, mentor junior colleagues, collaborate with engineering, and publish research.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

Convert raw workshops, interviews and long-form video into high-volume micro-content, sizzles, courses and high-converting landing/funnel pages using AI tools. Manage asset libraries, QA, reporting, CRM integrations and rapid revisions to meet SLAs.

Senior technical leader providing high-level AI strategy, architecture direction, and long-term systems design. Delivers strategic artifacts (architecture reviews, AI integration strategies, roadmaps), advises executives, collaborates with product and engineering teams, and mentors technical leaders to improve scalability, resilience, and governance across platforms.

The Test Lead will drive QA and validation strategy for AI solutions in a regulated industry. Responsibilities include defining testing strategies, executing tests, coordinating with client teams, and managing test data and environments.

Build and ship Kotlin-based native Android features for an AI-enabled neobank app. Design clean user journeys, integrate backend APIs, improve performance and reliability, and apply on-device ML where beneficial.

Build and ship native Android features for an AI-enabled neobank using Kotlin and Jetpack Compose. Integrate backend APIs, improve app performance and reliability, use analytics and on-device ML (TensorFlow Lite), and collaborate with product and design to simplify financial user journeys.

As an AI Inference Engineer, you will optimize C++ systems for AI model inference on edge devices, collaborating with researchers to deploy and enhance models while ensuring runtime stability and performance.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

Design and implement cloud-native AI solutions, build relationships with clients, and optimize AI systems. Mentor others and lead architecture decisions.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

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.

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.

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.