Top Artificial Intelligence Jobs in Warsaw

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.

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.

Serve as a technical bridge between NVIDIA’s generative AI platform and AI-native customers. Guide diffusion model training and deployment, optimize image and video generation pipelines across single- and multi-GPU environments, diagnose vision workload bottlenecks, and provide guidance on model architectures, inference efficiency, video tokenization, and multimodal systems. Translate customer feedback into product improvements and contribute technical demos, workshops, and reference implementations for the EMEA developer community.

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.

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.

Designs, builds, evaluates, deploys, monitors, and improves machine learning and generative AI solutions. Responsibilities include data preparation, feature engineering, model lifecycle ownership, production troubleshooting, CI/CD, observability, model governance, responsible AI, and collaboration with engineers, architects, and business stakeholders.

Lead the end-to-end design, development, deployment, and maintenance of production AI and machine learning solutions. Define architecture standards, technical strategy, MLOps practices, governance, responsible AI controls, and AI Act compliance. Build and monitor models, advise stakeholders, troubleshoot production systems, and mentor engineers. The role also supports Generative AI, LLM applications, document processing, RAG, and scalable cloud-based ML platforms.

Lead backend development using PHP and Laravel, owning features from design through deployment and monitoring. Build robust APIs, optimize databases and system performance, and improve reliability and security. Integrate agentic AI workflows, including LLM agents, tool-calling pipelines, retrieval, and autonomous task runners, into products and internal tools. Mentor engineers, collaborate across teams, and ensure production-ready code through testing, debugging, and quality practices.

Drive the global People & Culture AI and automation roadmap, identify and prioritize use cases, strengthen HR processes and data foundations, build business cases, and lead transformation initiatives through implementation and adoption. Evaluate Workday and other HCM capabilities, advise on technology and vendor choices, and support responsible AI governance, privacy, security, and regulatory readiness. Engage stakeholders, facilitate change, and translate HR, technology, data, and employee experience needs into scalable solutions.

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, build, and operate an enterprise AI developer platform enabling LLM routing, agentic workflows, RAG pipelines, observability, secure multi-tenant APIs, and GitOps deployments. Lead platform initiatives, mentor engineers, and provide self-service developer tools and prompt/versioning systems.

Develop and optimize GPU- and CPU-accelerated numerical software libraries for AI, analytics, image processing, computer vision, and scientific simulation workflows. Responsibilities include designing scalable algorithms, performance tuning, benchmarking across architectures, collaborating on feature requirements and technical roadmaps, providing technical leadership, and improving library usability and performance.

Design, build and deploy low-code/no-code automation and AI solutions (Power Platform, Copilot Studio, AI Builder) for Merchant Acquiring Operations. Lead discovery, prototyping, UAT and production rollouts, ensure governance and compliance, deliver training, mentor citizen developers, measure adoption and refine solutions, and inform the automation roadmap.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

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.

Lead a hands-on platform engineering team to design, operate, and scale enterprise AI platforms. Own architecture, reliability, vendor relationships, capacity planning, and compliance readiness. Partner with engineering, quality, and infrastructure to deliver consumable AI platform services for internal product teams, sponsor pods, and citizen developers in a regulated life-sciences environment.

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.

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.

Design and own the enterprise metadata and data discoverability architecture for a Databricks Lakehouse. Implement Active Metadata, Unity Catalog governance, lineage, tagging, and security; integrate metadata with BI and AI systems; evaluate Databricks features to automate metadata flows; provide technical leadership and mentor engineering teams.

Build, deploy, and productionize enterprise-grade AI solutions and agents using Snowflake AI Platform. Architect scalable ML pipelines, data models, ETL/ELT processes, and observability. Partner with customer data science and engineering teams to translate business needs into production systems, iterate rapidly, and ensure operational excellence and performance at scale.

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.

Designs, reviews, and operates production-grade security automation and AI capabilities. Builds API integrations, agentic workflows, CI/CD pipelines, and orchestration systems supporting incident response, threat intelligence, detection engineering, and threat hunting. Establishes engineering, safety, reliability, auditability, and change-management standards while partnering with security teams. Evaluates AI and automation technologies, participates in on-call support, and enables scalable, maintainable security operations platforms.

Lead a company-wide transformation embedding AI and agentic workflows into Product and UX. Define the vision and roadmap, design and pilot AI-enabled product lifecycle practices, create scalable playbooks and governance, assess tools, coordinate cross-functional workstreams, measure adoption and impact, and align Product, UX, Engineering, and leadership. The role requires hands-on generative AI experience, strong product judgment, technical fluency, program execution, stakeholder management, and the ability to influence teams through ambiguity.

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.