Top Artificial Intelligence Jobs in Poland

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Partner with EMEA AI/ML teams to drive adoption of Arize's observability platform, manage accounts through onboarding and renewals, run workshops/QBRs, identify upsell opportunities, and coordinate technical resources to ensure customers achieve measurable AI outcomes.

Customer-facing ML/GenAI engineer who advises customers, runs demos and strategic reviews, drives product adoption, partners with sales and engineering, and helps expand accounts by implementing best practices for production ML/LLM systems.

Design and deliver production-ready AI solutions for insurance, focusing on agentic AI, multi-agent systems, LLMs, RAG pipelines, and computer vision. Research and implement state-of-the-art models, take prototypes to production, collaborate with data scientists and engineers, and help build proprietary insurance-focused AI models for international markets.

The Principal AI Engineer will solve high-impact problems using AI, develop end-to-end solutions, and influence technical direction across teams.

Support integration of AI and automation across the data platform, build ETL and data quality checks using Dataiku, prepare data for reporting and analysis, improve reporting via ML and automation, collaborate with analysts and stakeholders to deliver reliable AI/data solutions and identify data improvement opportunities.

Lead product ownership for Data, Analytics & AI, driving roadmap, stakeholder alignment, and telephony/AI migrations. Deliver conversational AI and VUI solutions, oversee model lifecycle, testing, and integrations (Genesys, Salesforce, Snowflake) while influencing adoption across decentralized global markets.

The Principal Test Automation Engineer will design and implement testing strategies, apply AI tools for quality, enforce testing standards, and integrate testing into CI/CD processes.

Build production-grade AI and automation systems to integrate ad platforms, CRM and analytics for Performance Marketing. Ensure data accuracy by reviewing SQL and data models, create monitoring/diagnostics and human-in-the-loop decision workflows, and roll the solution out across nine markets.

Lead a team of senior full-stack developers to design, implement, and deliver scalable web applications using Next.js, React, TypeScript, Prisma, and PostgreSQL. Drive architecture, database design, API integrations (Stripe, Supabase, music platforms), AI-assisted development workflows (Cursor IDE), deployment, testing, and mentoring to ensure high-quality end-to-end delivery.

Lead engineering excellence for Digital Assets and AI initiatives, ensuring architectural consistency, risk management, and continuous improvement in engineering practices.

The role involves crafting high-quality, narrative-driven video content using AI tools, managing end-to-end video production, and maintaining character consistency in AI-generated outputs.

Design and develop software solutions for AI/ML applications, propose new approaches to optimization problems, and collaborate with global teams in research and development.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Design and deliver AI agent ecosystems, collaborate with teams on business goals, and provide mentorship while staying current with AI advancements.

Own lifecycle marketing campaigns across email, push and in-app channels: write copy, build automated customer journeys and behavioral triggers, run A/B tests, analyze performance with product analytics, and work with Product and Tech to optimize activation, retention and monetization.

The AI Software Developer will design scalable applications using Large Language Models and STT technologies, collaborating with cross-functional teams while writing clean, maintainable code.

Execute structured prompt testing, prototype lightweight AI workflows, configure and validate custom GPTs, document prompt libraries and exemplars, provide coaching and office-hour support, track usage patterns, and contribute technical input to enable responsible AI adoption across the firm.

The Process Efficiency Manager will lead continuous improvement initiatives, utilizing Lean Six Sigma methodologies, data analysis, and stakeholder management to drive operational excellence.

The Lead Software Engineer will design, develop and implement software solutions while mentoring junior engineers and ensuring high software quality in a collaborative environment.

This role involves designing, developing, and deploying AI and automation solutions, ensuring integration with enterprise systems, and meeting security standards. The position requires hands-on engineering skills in AI development, particularly with Python and AI frameworks.

Lead hands-on development and deployment of AI use cases to streamline Salesforce administration and enhance sales workflows. Deploy and evaluate models (e.g., Gemini, Agentforce, LLMs), integrate multi-source data, build safe AI integrations with Salesforce, collaborate with Salesforce IT and cross-functional teams, and ensure data privacy, security, and responsible AI usage to reduce manual administrative effort.

Design, build, and operate GitOps-driven data and AI platforms and pipelines (ETL/ELT, feature stores, batch/real-time inference). Enable GenAI/RAG (embeddings, vector ingestion), implement IaC and CI/CD, enforce data quality, governance, observability and MLOps, and support Azure/AWS-based production deployments while collaborating with security, product, and operations teams.