Top Artificial Intelligence Jobs in Poland

Drives value realization for software, automation, digital engineering, and AI investments. Develops ROI and business case analyses, defines KPIs and success criteria, tracks benefits against plans, evaluates technology use cases, and prepares executive dashboards and investment recommendations. Partners with engineering, finance, product, architecture, PMO, and transformation teams to prioritize investments, validate post-implementation benefits, and improve governance practices.

Owns planning, activation, optimization, testing, budgeting, and reporting for paid campaigns across TikTok, Meta, and YouTube. Manages $1M+ budgets, presents data-driven recommendations to clients, tracks platform changes, and partners with strategy and creative teams. The role also mentors a Paid Media Executive and establishes team standards for campaign QA, reporting, naming conventions, and optimization practices.

Design, implement, and optimize end-to-end AI pipelines including model training, deployment, monitoring, and inference. Build IaC and CI/CD workflows, ensure secure compliant production deployments, design deep learning models for NLP/vision/speech, troubleshoot performance, and mentor junior AI/Ops engineers while collaborating with stakeholders.

Own end-to-end features across Go backend services and React/TypeScript frontend applications. Design, develop, and debug high-load distributed systems, APIs, integrations, and event-driven services. Use AI coding agents as part of daily development, critically reviewing and verifying generated output. Contribute to architecture decisions, resolve complex production issues, and improve engineering practices for AI-assisted delivery.

Deliver AI fluency training, coach engineering teams on AI-assisted development, pair with developers, maintain internal AI agents and workflows, create enablement content, assess adoption maturity, and track productivity improvements. Collaborate with Architecture and Platform teams to support AI-first engineering practices and broader developer experience initiatives across the software delivery lifecycle.

Owns the development, validation, and continuous improvement of AI-powered analytics solutions, including Talk2Data and agent-ready data. Defines analytical requirements, evaluation metrics, quality frameworks, and data models; validates large datasets and AI outputs; collaborates with data engineers and business stakeholders; supports governance, deployment, enablement, and proof-of-concept initiatives; and evaluates AI tools and integrations.

Own the AI cost visibility product end-to-end, building Go backend services, APIs, and TypeScript/React interfaces. Translate cloud AI spend and usage data into clear FinOps experiences, while driving architecture, reliability, monitoring, and iteration. Collaborate closely with customers and product stakeholders, use AI throughout the engineering workflow, mentor peers, and shape product direction in an ambiguous market.

Own the AI cost visibility product end to end, developing Go backend services, APIs, data processing, and TypeScript/React interfaces. Translate AI spending data into clear FinOps experiences, work closely with customers and product needs, and use AI throughout the engineering workflow. Lead feature design, implementation, release, monitoring, and iteration while setting engineering standards, mentoring peers, and building scalable cloud-based SaaS capabilities.

Own AI cost visibility features end-to-end, from customer problem discovery and system design through implementation, release, monitoring, and iteration. Build scalable Go backend services, APIs, and TypeScript/React interfaces that process and present cloud AI spend data. Apply AI throughout the engineering workflow, contribute to product direction, understand FinOps and AI economics, mentor peers, and uphold reliable engineering practices.

Lead finance for a fast-growing AI-native B2B SaaS company, partnering with C-level leaders on strategic decisions. Manage a three-person controlling and accounting team; own budgeting, forecasting, SaaS KPI reporting, cash flow, FX hedging, accounting structures, intangible asset capitalization, and cost optimization. Provide financial modeling, profitability analysis, business recommendations, and tax collaboration while maintaining strong accounting and reporting standards.

Translate legal expertise into AI-powered workflows by designing, configuring, and testing Libra Assistants. Partner with clients to map processes, run pilots, enable adoption, and provide product feedback to improve prompt quality and accuracy. Create learning materials and drive high customer satisfaction while working cross-functionally with Product, Engineering, Sales, and Customer Success.

Leads the design and delivery of secure, scalable, LLM-enabled applications spanning APIs, user interfaces, orchestration pipelines, vector databases, and enterprise integrations. Builds reusable AI tooling, SDKs, and governance processes; deploys solutions across cloud environments; ensures observability, operational excellence, and responsible AI compliance. Partners with business stakeholders, architects, security teams, and strategic vendors to prioritize high-value use cases, guide platform strategy, and deliver production-ready systems.

Build and enhance RAG systems, AI components, backend services, data pipelines, REST APIs, evaluation harnesses, and production AWS deployments. Write tests, investigate model failure modes, support containerized CI/CD releases, document solutions, and contribute to architecture and client handoffs. The role offers senior guidance while progressively increasing ownership of technical decisions and system components.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Grow Qure’s user base from scratch by testing acquisition channels, managing budgets, creating marketing assets, building measurable funnels, and partnering with product on activation-focused improvements. The role requires rapid experimentation across paid, organic, community, video, and content channels, using data to optimize acquisition economics and eliminate underperforming efforts.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failure modes, and contribute to architectural decisions. The role offers increasing ownership while collaborating with senior engineers, FDEs, FDXs, and enterprise clients in distributed teams.

Build and operate production LLM-powered internal tools, automations, and customer-facing AI features. Own technical decisions, backend services, multi-service architectures, databases, containerized workloads, monitoring, reliability, incident response, and LLM evaluation workflows. Partner with product, security, and R&D teams while balancing AI quality, latency, and cost. Evaluate emerging AI technologies and contribute to reliable, secure production systems.

Leads the development and deployment of AI and data solutions for commercial teams across Europe. The role identifies business opportunities, builds scalable data products on Databricks and other cloud platforms, uses Claude tools to deliver analytics and insights, monitors solution performance, promotes AI adoption, and partners with stakeholders to drive measurable business impact.

Design and develop an AI-powered analytics agent in Devin, integrating GitHub, dbt, Databricks, Snowflake, and data ingestion systems. Adapt dbt workloads for Databricks, resolve platform compatibility issues, build and test agent capabilities, and implement guardrails, validation, observability, security, and reliable AI-assisted workflows. Collaborate with client engineering teams, troubleshoot integrations, document the solution, and prepare knowledge-transfer materials.

Develop and deploy deep learning, computer vision, Vision-Language Model, and generative AI solutions for retail imagery. Build AR and spatial computing features using 2D/3D geometry, optimize models for edge devices, benchmark performance, and scale robust algorithms into production across global deployments.

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.

Build and improve production-oriented AI applications, including agents, RAG pipelines, LLM integrations, and data applications. Develop evaluation frameworks, test datasets, observability, safety guardrails, monitoring, and human-review workflows. Collaborate with AI engineers, researchers, customers, Product, and Engineering teams while writing tested Python and SQL. Participate in software development activities such as design reviews, code reviews, debugging, documentation, deployment, and experimentation.

Lead hands-on development of AI-powered workflows, coding-agent integrations, and C++ development capabilities for CLion. Design agent architectures, tool integrations, benchmarks, and evaluation frameworks while prototyping debugging, code understanding, refactoring, and navigation workflows. Collaborate with JetBrains AI, ML, product, QA, and C++ teams to shape technical direction, roadmap priorities, and AI strategy for developer tools.

Design, build, and deploy AI agents and conversational solutions using Microsoft Copilot Studio and Power Platform. Integrate LLM-based, RAG-enabled systems with enterprise APIs, implement monitoring/analytics, ensure security and governance, and collaborate with stakeholders in Agile delivery.

Lead EMEA customers in deploying and optimizing large-scale AI inference on multi-node GPU clusters. Architect dense and Mixture-of-Experts inference pipelines, improve quantization, speculative decoding, prefill/decode disaggregation, KV cache management, and workload scheduling. Collaborate with NVIDIA product teams and engage the developer community through workshops, hackathons, and reference architectures. Advise ML engineers, researchers, and systems architects on high-performance inference across thousands of GPUs.