Top Artificial Intelligence Jobs in Poland

Develop and optimize multimodal AI solutions for document intelligence, personalization, and image/video analysis. Build production-ready pipelines, guide customers on model training across modalities, optimize vision encoders and video processing for latency, and translate customer needs into NVIDIA product roadmaps. The role also involves technical relationship management, developer engagement through talks and hackathons, and creating demos and reference architectures.

Serve as the senior technical expert for EMEA AI organizations training and post-training large-scale foundation models. Advise customers on distributed training infrastructure, PyTorch, Megatron-LM, NeMo, reinforcement learning, GPU utilization, communication overlap, and memory management. Build strategic technical relationships, translate customer needs into NVIDIA product roadmap input, collaborate with research and product teams, and support hackathons, demonstrations, and technical conferences.

Lead the architecture, development, deployment, and optimization of production-grade Generative AI systems, including LLM applications, agents, RAG, and multi-agent workflows. The role combines hands-on backend and AI engineering with technical leadership, architecture, mentoring, evaluation, observability, responsible AI, and cross-team influence. Responsibilities include building scalable services and platforms, integrating models and tools, improving reliability and performance, and guiding complex AI initiatives from exploration through production operations.

Build and operate production-grade AI agents and agentic workflows for ABB digital services and asset management. Integrate agents with enterprise platforms, APIs, operational data, and digital tools. Define orchestration, context, memory, evaluation, safety, monitoring, deployment, and control mechanisms. Partner with architects, product owners, data engineers, and software teams while mentoring colleagues and ensuring secure, scalable, reliable, and cost-effective enterprise solutions.

Executes prompting frameworks and scale-up plans for Agentic AI use cases. Develops and refines prompts, coordinates testing and defect remediation, embeds AI capabilities into BAU operations, creates SOPs and runbooks, supports training, manages prompting backlogs, and reports on quality, adoption, and delivery progress across cross-functional global teams.

Build and maintain Databricks data pipelines, curated datasets, metric views, and governed analytics systems. Develop Genie Agents and multi-agent orchestration, implement MLflow tracing and evaluation, integrate AI tools through MCP, and collaborate with data and product teams to create reusable business definitions and reliable production-grade analytical solutions.

Develop AI and machine learning solutions for ADAS and automated driving systems. Apply computer vision, deep learning, multimodal learning, foundation models, and temporal modeling to automotive datasets and production applications. Implement, train, and evaluate models, translate research into practical systems, and collaborate with researchers and engineers. Work may involve perception, prediction, planning, autonomous systems, model optimization, and deployment for customer vehicles.

Design, build, deploy, and operate AI agents and automations for GTM and Finance workflows. Develop MCP integrations, local LLM environments, RAG pipelines, and agent harnesses with orchestration, tool-calling, memory, guardrails, logging, and evaluation. Write production-quality Python, integrate business systems, establish monitoring and testing, and document architectures and runbooks.

Design and improve LLM-powered contract intelligence workflows, including prompt engineering, structured extraction, evaluation, hallucination detection, and exception handling. Translate legal reasoning and contract expertise into schemas, taxonomies, guidance, and production-ready AI instructions. Collaborate with Product, Engineering, Data Science, Legal, and Operations teams to deploy scalable solutions, establish quality and governance frameworks, manage ambiguity and edge cases, and measure operational impact.

Review, evaluate, and annotate AI-generated text, images, audio, and video; perform quality checks; identify inconsistencies and edge cases; support dataset development; participate in calibration; and provide feedback to improve annotation workflows and guidelines.

Design and implement cloud and platform solutions (AWS/Azure) using Terraform, integrate Generative AI/LLM technologies with enterprise tools (Grafana, Splunk, Collibra, Kubernetes, Envoy, LiteLLM), collaborate with architects and stakeholders to define integration patterns and scalable designs, and provide hands-on implementation and technical consulting within Agile teams.

Client-facing AI Analyst responsible for eliciting executive strategy, OKRs, investment policies, and operating processes; defining AI agent skills and evaluations; interpreting process-mining data; modeling ROI; prototyping LLM solutions; driving executive adoption; and documenting requirements, decisions, and runbooks. The role partners with executives, an AI Architect, and a Data Engineer to deliver an AI context layer and executive pilot.

Leads practical AI enablement across the organization through learning programs, workshops, e-learning, AI adoption initiatives, process optimization, and internal consulting. Translates technical AI concepts for nontechnical audiences, supports AI communities and champions, develops guides and use cases, and partners with the AI Center of Excellence on responsible AI governance, privacy, security, and adoption measurement.

Develops and deploys scalable AI and machine learning systems for precision oncology. Responsibilities include building multimodal data platforms and pipelines, model-training workflows, inference services, MLOps processes, APIs, and production software. The role applies foundation models, LLMs, generative AI, and vision-language models to clinical and research data while collaborating with scientists, clinicians, and informaticians to improve cancer research and patient outcomes.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.

Designs and maintains security controls for AI platforms, models, data pipelines, and inference environments. Embeds AI security requirements into engineering, MLOps, operational workflows, enterprise platforms, and CI/CD pipelines. Identifies and mitigates AI-specific risks including model poisoning, data leakage, adversarial inputs, and generative AI misuse. Partners with SOC, IAM, cloud, platform, and security management teams, while developing security roadmaps and managing specialist vendors.

Create finished AI-generated video scenes and sequences for short-form mobile dramas. Translate scripts into visual storytelling, combining AI-generated images, video, and sound into coherent content. Work independently, follow creative direction and production standards, deliver complete AI-driven projects, and help improve production workflows while collaborating with senior creative leads.

Create finished AI-generated video scenes and sequences for short-form mobile dramas. Translate scripts into visual content, combine AI-generated imagery, video, and sound, and develop coherent visual storytelling. Work independently on creative assignments, improve AI production workflows, and collaborate with senior creative leads. A relevant portfolio, including a 1–5 minute AI video or experimental film, is required.

The Implementation Consultant will advise and configure AI legal solutions, manage client engagements, train users, and deliver tailored implementations for legal departments.

Design, develop, and own production-grade backend features from architecture through deployment. Build robust, scalable server-side systems for complex performance requirements, maintain code infrastructure, and integrate large language models, vector databases, and third-party AI services to create AI-powered APIs and services.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

Designs production-ready AI architectures for enterprise customer experience and contact center use cases. Responsibilities include RAG and knowledge architecture, AI evaluation and governance, contextual workflows, scalability and latency optimization, prototypes, third-party integrations, technical demonstrations, sales support, field enablement, and executive stakeholder influence.

Lead and develop a team of eight software engineers responsible for Netflix’s Graph Search platform. Define the technical and product vision for search, natural-language search, and AI-enabled capabilities; align roadmaps across teams; improve platform adoption, reliability, performance, observability, and self-service onboarding; and advance safe AI-assisted engineering practices. Partner with product and engineering stakeholders, prioritize investments based on measurable impact, and build an inclusive, accountable team culture.

Build and own cloud features end to end across backend services, React interfaces, databases, testing, CI/CD, deployment, and production support. Use AI coding tools to accelerate development, debugging, refactoring, and test generation. Collaborate with product and UX teams to deliver scalable, reliable customer experiences using public cloud platforms, SQL/NoSQL databases, and modern infrastructure.