Top Artificial Intelligence Jobs in Warsaw

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Leads digital transformation across procurement by implementing and optimizing SAP, Coupa, ERP, e-sourcing, and supplier management technologies. Oversees data governance, integration, process improvement, category and spend analytics, supplier performance monitoring, and cross-functional project delivery. Evaluates emerging capabilities such as AI and RPA, manages stakeholder relationships, mentors team members, and drives procurement technology innovation and operational efficiency.

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Lead Accenture's global Data & AI compliance program: review internal and client projects, translate legal/regulatory AI and data requirements into policy, work with risk and security teams, advise contracting counsel, develop guidance and training, monitor trends, and build stakeholder relationships to operationalize compliance across services and offerings.

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.

Own the AI agenda across Customer Operations by identifying, implementing, monitoring, and optimizing AI features such as chat and email agents. Manage multiple concurrent projects, align stakeholders, translate operational pain points into automation opportunities, and improve processes across Customer Support and Payments, Fraud & Verification. Track post-launch performance, resolve issues, and drive continuous operational excellence through analytical, creative, and cross-functional problem-solving.

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

Analyze and investigate customer matches against sanctions and KYC/AML lists during account opening and maintenance. Conduct searches, gather and record evidence from internal systems, internet and commercial databases. Collaborate with Business and Compliance partners, operate within agreed timeframes, apply training, build sanctions/KYC knowledge, and use AI/ML screening tools to support alert disposition.

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.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, including LLM inference infrastructure, RAG, AI agents, and evaluation/observability. Design and run experiments (A/B tests), optimize systems for production performance and reliability, and iterate rapidly with product and design partners.

Design and build GitLab Duo CLI features in TypeScript, shape AI-assisted developer experiences, improve CLI reliability, observability, and performance, collaborate across Developer Clients, and provide technical leadership and mentorship.

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.

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.

Partner with clients to identify Generative and Agentic AI opportunities, design scalable AI solutions and operating models, produce roadmaps and executive deliverables, support technical delivery and change management, and help embed AI capabilities and adoption across organisations.

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.

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.

Design and ship full-stack applications, AI agents, and GenAI platform components from concept through production. Build developer tooling, CI/CD, observability, evaluations, canary releases, and rollout systems. Apply secure SDLC, privacy-by-design, RAG, routing, tool use, and LLM evaluation practices. Lead architecture, mentor engineers, collaborate cross-functionally, and improve AI reliability, safety, latency, cost, and customer outcomes.

Participate in an AI and MLOps internship delivering software, data pipelines, infrastructure-as-code, backends, and user interfaces. Collaborate on Generative AI solutions, contribute to client-facing business decisions, and gain experience in project management and team leadership. Use technologies including Docker, Terraform, AWS, FastAPI, LangChain, and Spark while developing cloud, machine learning, big data, and LLM expertise.

Own the AI platform roadmap and build reusable capabilities (prompting, RAG, tool calling, agents, evaluation, observability). Run discovery, prioritize AI investments, partner with Product, Engineering, Design and Data, guide other PMs, define success metrics, and ensure scalable, reliable AI-powered features that deliver customer and business value.

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.

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 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.