Top Artificial Intelligence Jobs in Krakow

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.

Design, build, and deploy production-grade AI workflows and intelligent agents that automate cross-functional business processes. Translate stakeholder needs into scalable integrations, track adoption and outcomes, run experiments, and iterate to deliver measurable efficiency gains across Sales, Finance, Support, and Operations.

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 Sales Operations & AI Manager will oversee CRM systems and automations, optimize sales processes, and build AI workflows to enhance lead generation and data management.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

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

Polish Dubbing AI Adaptors oversee dubbing projects, lead casting, drive script adaptation, and ensure quality assurance in productions.

Build and ship agent orchestration workflows, integrations, and shared infrastructure for AI-driven growth across paid media, lifecycle, organic content, CRO, incentives, and referrals. Implement monitoring, audit logging, creative generation pipelines, and reliable integrations so domain experts can tune and operate workflows independently.

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.

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.

As a Senior AI Engineer, you will develop AI-powered features, integrating LLM APIs to enhance user workflows while collaborating with teams to ensure reliability and scalability.

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.

The AI Go-to-Market Engineer will deploy and optimize AI systems across revenue operations to enhance productivity, pipeline generation, and minimize churn, reporting directly to the CEO.

Design, build, orchestrate, and deploy production-ready AI agents and LLM-based solutions. Partner with domain experts to identify high-value use cases, redesign workflows using AI-first principles, integrate AI workflows into business infrastructure, and support internal AI enablement and training.

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.

The role involves leading design for AI-native products at Synthesia, focusing on improving interaction patterns and enhancing user experience in AI-driven workflows.

Lead a Special Projects engineering team to explore and implement Generative AI and cloud solutions. Hands-on development in Python/Java, collaborate with product and data science, design and deploy ML/LLM solutions to production on GCP/Vertex AI, and mentor engineers while promoting continuous learning.

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.