Top Artificial Intelligence Jobs in Warsaw

Build and iterate AI systems spanning computer vision, image manipulation, video generation, and agentic data analysis. Own the full pipeline from model research and API integration through scalable web application deployment. Develop rapid MVPs using AI-assisted tools, orchestrate multiple models into autonomous workflows, and test solutions in e-commerce operations before scaling them into SaaS products.

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.

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.

Lead engineering activities for productizing Deep Learning models, mentoring team members, collaborating across units, and optimizing DL workloads.

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.

Build reusable AI developer tools, RAG knowledge systems, and AI-assisted SDLC workflows. Integrate coding assistants and design-to-code tooling, define secure observable patterns, and own reliability, usage, and cost for internal AI platforms.

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.

Own a core construction software product area from customer problem through measurable outcome. Responsibilities include customer discovery across construction sites and commercial teams, product strategy and roadmap ownership, prioritization, AI feature development and evaluation, human-in-the-loop design, stakeholder communication, and raising product-management capability. The role partners closely with engineering, design, consultancy, growth, and customer success teams while driving adoption and business impact.

Design, build, and deploy AI-powered products using frontier LLMs. Create agents, RAG systems, and backend services; evaluate and improve model quality, ensure trust/privacy, and collaborate with product, design, and finance teams.

Build and own production features for Lyro, Tidio’s AI customer support agent. Responsibilities include developing integrations and distribution channels, improving context engineering, tool use, prompt caching, and client-facing evaluation systems. The role spans the full stack, requiring end-to-end ownership from scoping and stakeholder collaboration through deployment, monitoring, and iteration. Engineers work autonomously in a fast-paced environment and use AI-assisted, spec-driven development.

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.

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.

Integrate and maintain third-party carrier inventory across platform tools and integration modes. Identify repetitive onboarding and inventory tasks, then replace them with scripts, workflows, no-code automation, and AI solutions. Use data to identify automation opportunities and measure impact, maintain data quality, troubleshoot technical issues with engineers, and improve partner and customer experiences in a cross-functional international team.

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.

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.

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.

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.

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.