Top Artificial Intelligence Jobs in Romania

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.

Lead the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

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.

Lead design and governance of ICON's end-to-end AI platform, multi-agent frameworks, and supporting services. Define cloud-native, scalable architectures, integration patterns, APIs, and standards; lead solution architecture, design reviews, and mentor engineering teams to deliver secure, high-performing AI/ML production systems.

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.

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.

The BI Engineer will create and maintain dashboards, support analysis, ensure data integrity, and contribute to data strategy initiatives.

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.

Design, develop, and deploy ML solutions for localization workflows (machine translation, LLM fine-tuning, QA). Own projects end-to-end, implement and optimize models using Python and ML frameworks, deploy via Docker and AWS (SageMaker/EC2/S3), monitor production, document experiments, and collaborate across teams.

Build and operationalise GenAI-enabled web applications: implement frontend and backend features, integrate LLMs (RAG, prompt engineering), collaborate with AI teams, hand over production-ready prototypes, and maintain best-practice development standards in an Agile environment.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

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.

Design and scale GTM systems and AI-powered workflows across sales, marketing, and product. Build agents, integrate Salesforce/HubSpot, turn product and user data into growth signals, create lead-scoring/routing/onboarding flows, identify bottlenecks, run experiments with founders to drive measurable revenue impact.

Support design, maintenance, and operation of SCOR's AI governance framework, ensuring regulatory readiness (EU AI Act), risk management, standards, playbooks, and oversight of internal and client AI solutions. Collaborate with legal, compliance, risk, data, and technology teams to embed ethical, secure, and pragmatic AI guardrails, assess AI risks (bias, explainability, robustness), and promote governance-by-design and enablement across the organization.

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.

Design, build, and operate an internal AI Background Agents Platform: implement secure stateful runtimes, sandboxing, CI/CD and GitOps pipelines, progressive delivery, observability (tracing/monitoring), and developer-facing tools to enable teams to build, test, and deploy autonomous LLM-backed agents.

Design and build the ALES platform AI core: architect agentic generative models to create and simulate realistic ATC scenarios from large flight and weather datasets, develop self-learning systems, integrate with production stacks, collaborate with ATC experts and cross-functional teams, and deliver scalable, tested deployments that improve V&V, training, and operational planning.

Design, build, and operate scalable AI/ML platforms: training, inference, pipelines, feature stores, MLOps automation, GPU optimization, monitoring, security/governance, LLM evaluation, RAG and agent development, and cross-functional enablement to productionize models.

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.