Top Artificial Intelligence Jobs in Krakow

Defines enterprise AI solution architecture across generative AI, predictive analytics, NLP, and computer vision. Develops scalable architecture blueprints, reference patterns, and implementation roadmaps; leads design governance for data pipelines, model integration, APIs, orchestration, deployment, and observability. Partners with product, engineering, data, and business stakeholders, while guiding AI/ML engineering, MLOps, model lifecycle management, responsible AI, security, and platform scalability.

Supports AI initiative delivery by identifying business opportunities, gathering requirements, translating needs into technical specifications, assessing business value, and coordinating stakeholders, data scientists, engineers, IT teams, and vendors. The role also supports Azure AI solution delivery, data governance, ethical AI compliance, change management, user adoption, documentation, and process optimization.

Leads the strategic direction, governance, financial management, and evolution of enterprise AI platform services. Establishes operating models, policies, compliance controls, responsible AI practices, consumption and cost management strategies, and model lifecycle governance. Manages vendors, contracts, licensing, and strategic technology investments while partnering with security, legal, procurement, finance, and compliance teams. Builds and leads a multidisciplinary global team focused on AI platform governance, FinOps, risk management, and service portfolio management.

Leads global engineering and operations for an enterprise AI platform, overseeing architecture, cloud infrastructure, integration, automation, security, governance, reliability, and lifecycle management. Drives DevOps, MLOps, LLMOps, and AIOps maturity while managing service operations, incident response, releases, and operational risk. Builds and develops multidisciplinary global teams and partners with senior business, technology, cybersecurity, and vendor stakeholders.

Build and launch customer-facing AI product capabilities from concept through production, measurement, and iteration. Partner with Product, Design, Data, Security, and platform teams to create reliable, measurable, trusted AI experiences. Responsibilities include evaluating probabilistic systems, designing failure paths, monitoring adoption and quality, supporting production readiness, and using customer feedback to improve, pause, or retire features.

Own AI platform capabilities from design through production operation, including LLM gateways, retrieval systems, agents, developer tooling, evaluation frameworks, and guardrails. Establish SLOs, observability, security, multi-tenant isolation, and reliable operational processes. Drive adoption through clean interfaces, migration paths, documentation, and reusable patterns while participating in on-call, incident response, and continuous improvement.

Design and deliver complex AI-powered automation systems using n8n, Make.com, HubSpot, JavaScript/TypeScript, and Python. Build integrations, AI agents, RAG systems, workflows, and custom applications involving APIs, databases, webhooks, authentication, LLMs, and vector databases. Own projects from requirements and architecture through deployment, troubleshooting, optimization, and documentation while collaborating with the consultancy team.

Own the full enterprise B2B sales cycle for Sana’s AI products in Poland, from prospecting and opportunity development through solution shaping and deal closure. Build self-sourced pipeline, exceed quotas, close $10K–$100K ACV opportunities, and engage C-level decision-makers. Partner with product, engineering, and strategy teams to align solutions with customer needs and expand Sana’s market presence.

Embed with client teams to identify operational workflows suitable for AI redesign, translate business problems into technical plans, and deploy production AI systems. Build agentic systems, RAG and data pipelines when needed, establish telemetry and evaluation feedback loops, manage stakeholders, and train client teams to operate and improve the solutions. Commercial Banking, Financial Services, or Insurance experience is required.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Own end-to-end product and operational analytics for AI-powered and conventional automation features. Analyze workflows, logs, transcripts, and generated outputs to identify failures, quality degradation, adoption patterns, and business impact. Design AI evaluations, human review processes, monitoring, dashboards, and experiments, then translate findings into product and engineering improvements. Partner with Product, Engineering, Clinical, MedOps, and Legal in an ambiguous, fast-moving medical technology environment.

Architect and lead implementation of an enterprise Generative AI Lakehouse platform on Databricks. Responsibilities include building document ingestion and structured extraction pipelines, enforcing anti-hallucination guardrails, developing Human-in-the-Loop review applications, integrating ERP feeds, and establishing governance, security, lineage, and auditability. The role also involves client consultation, architectural documentation, mentoring, and translating complex GenAI solutions for technical and executive stakeholders.

Build and operate production AI services, including multi-agent systems, LLM pipelines, MCP servers, RAG and document-ingestion workflows. Develop reliable Python backend services, evaluate model quality, manage deployments, observability, authentication and privacy safeguards. Collaborate with engineering and product teams while supporting AI governance, risk documentation and ISO evidence. The role focuses on practical business automation across document processing, customer support, CRM and account operations.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Own the vision, roadmap, architecture, and hands-on delivery of IFS’s Internal Developer Platform. Build self-service scaffolding, golden paths, service catalogs, developer portals, CI/CD integrations, authentication, observability, and platform standards. Drive adoption using developer feedback, usage analytics, onboarding metrics, platform NPS, and DORA metrics. Collaborate across distributed engineering teams, influence platform strategy, and mentor senior engineers without direct management responsibility.

Design and deliver production-ready Generative AI architectures, including LLM, RAG, agentic, automation, and vector-search solutions. Translate client business needs into scalable cloud architectures, proposals, diagrams, and implementation plans. Lead workshops, technical presentations, and AI pre-sales activities while guiding engineering teams through delivery. Apply AI governance, security, responsible AI, deployment, monitoring, and lifecycle-management best practices.

Develops World Vision International’s policy positions and external influence on children’s digital rights, AI governance, online safety, and digital participation. The role analyzes global regulatory developments, produces thought leadership and advocacy content, represents the organization in policy forums, leads coalition engagement, and advises internal teams and senior leaders. It requires collaboration across international teams and travel of up to 25%.

Build BLP’s core software across infrastructure, backend, machine learning, and product extension platforms. Responsibilities include operating secure and observable cloud infrastructure, developing performant distributed systems, applying AI to business problems, and enabling customer process automation. The role emphasizes formal reasoning, correctness by construction, maintainable systems, type systems, and functional programming. Work is available onsite in Zurich or remotely within Europe.

Build and operate production AI infrastructure powering intelligent agents at scale. Develop high-performance Rust services for model inference, orchestration, and execution; design scalable distributed systems; establish MLOps, observability, guardrails, and failure recovery; and optimize latency, throughput, reliability, and cost. Partner with agent systems and infrastructure teams to turn prototypes into hardened services serving millions of users.

Build and deploy reliable, measurable, and safe AI agents for real business workflows. Work within Marketing Engineering and growth pods to identify problems, develop end-to-end solutions, evaluate LLM performance, and measure business impact. The role involves productionizing agent systems beyond demos, using Python, SQL, LLM APIs, and orchestration tools, with potential integration into go-to-market data and marketing systems.

Conduct research, market sizing, forecasting, competitive analysis, and thought leadership on enterprise AI services. Develop actionable insights for technology providers and senior executives through published research, presentations, conferences, and client engagements. Assess market trends, demand drivers, customer behavior, and growth opportunities while advising clients, supporting sales, reviewing content, mentoring analysts, and improving research processes.

Lead and scale TRG’s engineering organization across Big Data, data fusion, AI, geospatial intelligence and OSINT. Own technical direction, architecture, execution standards and engineering culture while developing engineering managers and technical leaders. Partner with Product, Applied Research and executives to turn research and prototypes into scalable products. The role emphasizes innovation, product thinking, distributed systems, organizational growth, engineering productivity and maintaining startup speed while improving accountability, reliability and delivery.

Leads end-to-end architecture for enterprise data, analytics, and AI solutions in a Microsoft-first environment. Translates business requirements into secure, scalable designs and guides prototyping, implementation, architecture reviews, troubleshooting, governance, and production readiness. Requires deep hands-on experience with Azure data platforms, Power BI, enterprise security, Azure OpenAI, and retrieval-augmented generation patterns.