Top Artificial Intelligence Jobs in Romania

As a Customer Support Expert, you will provide customer support via social media and email, representing various brands while setting your own schedule and working remotely.

Lead cloud and AI architecture: define scalable, secure system designs, technology stacks, and workflows; guide DevOps and engineering teams; promote AI/ML best practices; mentor developers and ensure delivery alignment with business goals.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Lead branding and visual identity work for F&B concepts using traditional design and AI tools. Create logos, menus, signage, packaging, production-ready files, presentations, and brand guidelines while collaborating with cross-functional teams.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Lead design, development and deployment of scalable AI/LLM systems (RAG, agentic systems). Architect end-to-end ML pipelines, apply MLOps/LLMOps, integrate AI with enterprise platforms, ensure security/privacy/compliance, mentor teams, and drive presales and delivery for enterprise clients.

Lead end-to-end implementations of QVAC-powered decentralized AI solutions, coordinating clients, engineering, product, and legal. Translate business needs into deployment roadmaps, manage milestones and risks, ensure production readiness, and produce governance, documentation, and executive reporting across multiple concurrent projects.

The Technical Architect will design and oversee modular AI-optimised software systems, ensuring scalability and resilience, while mentoring teams in AI-assisted coding practices and architectural governance.

Lead complex financial analysis, forecasting, and capital strategy for HPC and AI business. Advise leaders on strategic decisions, drive process improvements and GAAP adherence, represent business in cross-functional planning, and mentor finance analysts.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Design and build production-grade AI applications and services in Python, integrating LLMs with backend APIs and enterprise systems, implementing evaluation frameworks, observability, CI/CD, and reusable components to ensure reliable, testable, and scalable AI features.

Design, build and deploy AI agents and end-to-end solutions using Microsoft Copilot Studio, Microsoft 365 Copilot, and Azure AI. Work with DACH customers to identify AI opportunities, translate business requirements into technical solutions, ensure security and governance, and deliver scalable automation that creates measurable business value.

Design, build, and own AI/LLM-assisted product features in Ruby on Rails: integrate LLM APIs, combine AI with business logic, tests, fallbacks and safeguards, improve reliability/latency/cost/security, write automated tests, and debug production issues.

As an AI/ML Engineer, you will develop and optimize AI solutions for renewable energy assets, collaborating with a skilled team to deploy algorithms in real-world projects.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The AI Engineer will design and develop AI-powered features, evaluate AI/ML services, and integrate them into a cloud-based platform, working closely with cross-functional teams.

Design, build and maintain AI-embedded Regulatory News applications across the full Agile development lifecycle. Define AI best practices, collaborate with product owners, perform design and peer code reviews, improve code quality, support releases, and reduce operational workload through AI-driven features.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

Define and deliver AI-driven features across HCM, TSCM and voyage compliance product lines. Identify AI/ML opportunities, translate models into product requirements, run discovery with customers, measure outcomes, ensure responsible AI practices, and partner with cross-functional teams to launch and commercialise solutions.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.