Top Artificial Intelligence Jobs in Krakow

Manage full-cycle technical recruiting for Engineering, Product, Security, UX, and R&D roles. Partner with hiring leaders on role definition, structured interviews, sourcing strategy, market research, candidate assessment, pipeline management, and offer negotiations. Build diverse talent pipelines, use recruiting data to guide decisions, maintain accurate Greenhouse documentation, deliver an inclusive candidate experience, and improve recruiting practices through responsible AI use and shared resources.

Manage full-cycle recruiting for go-to-market roles across sales, marketing, customer success, professional services, and related teams. Partner with hiring leaders, build diverse talent pipelines, conduct structured assessments, use market and funnel data to guide hiring, manage candidate experiences, negotiate offers, and maintain recruiting documentation. The role also involves improving recruiting practices, responsibly using AI, and advising stakeholders in a globally distributed, remote-first environment.

Leads and supports global oncology real-world evidence and epidemiology studies across drug development. Responsibilities include study design, literature synthesis, protocol and report authoring, observational data analysis, regulatory support, stakeholder presentations, external partnerships, peer review, compliance, and tracking project deliverables. The role generates evidence and insights for Pfizer oncology assets while collaborating with medical, regulatory, and cross-functional teams.

Enterprise Account Executive role focused on building new business across EMEA. Responsibilities include developing territory sales strategies, acquiring enterprise customers, exceeding new-logo quotas, managing complex consultative sales cycles, engaging C-level decision-makers, negotiating contracts, partnering with marketing and pre-sales, and developing partner go-to-market strategies. The role requires enterprise SaaS sales experience, technical platform selling expertise, and proficiency with Salesforce, Clari, and MEDDPICC.

Lead privacy engineering strategy and automation across the product lifecycle. Own Privacy by Design, DPIAs, privacy assessments, DSR processes, privacy controls, monitoring, metrics, and governance. Build scalable tooling and technical standards across cloud architectures, APIs, data platforms, and products. Advise Product, Engineering, Security, Architecture, Legal, and Privacy teams while applying privacy-enhancing technologies, AI governance, and regulatory frameworks.

Design, build, deploy, and optimize production-grade generative AI agents, copilots, and intelligent workflows. Lead end-to-end delivery from solution design and prompt engineering to deployment, monitoring, and continuous improvement. Partner with business stakeholders to transform processes for AI-first execution, define KPIs, ensure governance and security, provide training and change management, and contribute reusable frameworks and best practices across the AI Center of Excellence.

Design, build, and operate an enterprise AI Hub providing shared runtime, CI/CD, model registry, observability, governance, and developer tooling to enable scalable, secure production AI and self-service capabilities across cloud and hybrid environments.

Develop Responsible AI governance tooling, technical guardrails, risk assessments, MCP scanners, and compliance automation for AI systems and agents. Build production-grade software integrated with CI/CD pipelines, support OSS compliance workflows, improve security and reliability, and translate governance requirements into engineering solutions. The role also involves technical documentation, training, stakeholder communication, and establishing organization-wide Responsible AI practices.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and experimentation frameworks for production AI agents. Responsibilities include measuring agent quality, diagnosing failures, evaluating models, improving cost and latency, managing team priorities, and partnering with AI Core engineering. The role is hands-on and requires production software development experience with complex tool-using AI systems.

Lead a hands-on AI Quality engineering team building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Design experiments across prompts and models, improve quality, latency, and cost, diagnose agent failures, and partner with AI Core engineering. Manage team priorities while contributing directly to Ruby on Rails and Python code.

Lead a hands-on AI Quality engineering team building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and experimentation frameworks for AI agents. Responsibilities include designing CI/CD pipelines and scorers, diagnosing agent failures, evaluating models, optimizing cost and latency, and partnering with AI Core engineering to improve production AI quality. The role also includes team leadership and management.

Lead a hands-on AI Quality engineering team building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and experimentation frameworks for production AI agents. The role evaluates models, prompts, tools, and agent behavior while improving quality, latency, and cost. Responsibilities include leading technical direction, managing team priorities, coding, and partnering with AI Core engineering. Experience with production software, LLM agents, evaluation infrastructure, Ruby on Rails, Python, and ML/AI datasets is required.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Design experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering, manage priorities, and remain hands-on with implementation.

Lead a hands-on AI Quality engineering team building evaluation infrastructure, observability tools, diagnostic systems, datasets, annotation workflows, and experiment frameworks for production AI agents. Responsibilities include measuring agent quality, tracing failures, evaluating models, improving cost and latency, managing team priorities, and partnering with AI Core engineering. The role requires substantial production software experience, AI/ML evaluation expertise, and proficiency or adaptability in Ruby on Rails and Python.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

Business Analyst responsible for Market Risk data quality, end-to-end data lineage, process re-engineering, and AI/GenAI automation. The role analyzes data flows, change impacts, controls, and documentation while supporting FRTB and BCBS239 governance. Responsibilities include requirements gathering, process mapping, user stories, stakeholder management, and UAT coordination across Risk, Operations, Finance, and Technology teams.

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Lead AI Value Analyst responsible for building and maintaining business cases for AI initiatives, estimating and tracking benefits, and reporting ROI. Partners with stakeholders, Finance, and AI CoE to quantify productivity, cost, revenue, and risk impacts, prioritize opportunities, validate data and assumptions, and produce dashboards and presentations to communicate realized value and governance across the AI portfolio.

Build and lead production LLM systems, including agentic workflows, content enrichment, retrieval, semantic search, evaluation, observability, and safety guardrails. Own architecture decisions across model selection, prompting, fine-tuning, latency, cost, and quality. Partner with Product and UX to ship AI features, mentor engineers, lead technical reviews, and establish reusable LLM development practices.

Own the lifecycle of AI products by translating business priorities into executable roadmaps, partnering with engineering and design, driving delivery, and incorporating user feedback. Lead client demonstrations, define and evaluate product metrics, coordinate cross-functional teams, and align product development with broader innovation strategy. The role requires strong product management, leadership, communication, analytical, agile, and user research skills.

Lead CitizenGO’s advocacy campaigns in Poland, including researching issues, developing strategy, writing and translating communications, mobilizing supporters, and coordinating online and offline actions. Build coalitions with organizations, engage media and influencers, represent the organization publicly, manage petitions and campaign workflows, monitor website language accuracy, and support staff and AI-enabled processes. The role is remote with flexible hours but requires monthly travel for in-person events.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Work closely with company founders to execute go-to-market strategies, improve operational processes, grow client and partner communities, and implement AI-driven solutions. The role involves managing go-to-market pipelines end to end, coordinating cross-functional initiatives, and adapting within a fast-paced startup environment.

Supports immigration matters by preparing and filing petitions, assisting with U.S. talent visa processes, researching immigration laws and regulations, and collaborating with cross-functional teams on legal compliance. The role suits an organized, adaptable paralegal with immigration documentation experience who is interested in contributing to an AI-driven startup law firm serving international teams.

Leads OKX’s global marketing compliance function, building policies, controls, review and approval processes, monitoring programs, and regulatory change management for advertising, promotions, social media, influencers, affiliates, and product launches across CeFi and DeFi. Partners with Marketing, Product, Legal, Risk, and Compliance teams; manages incidents, supports audits, delivers training, and scales the compliance team and framework.