Top Artificial Intelligence Jobs in Romania

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.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

The AI & Automation Intern will assist in creating internal tools, AI workflows, and automations, while collaborating with management to enhance operations. Responsibilities include researching AI technologies, building applications, and documenting processes.

Design and build code-first intelligent automation solutions using Python, C#/.NET and TypeScript. Develop GenAI-driven agentic workflows, UI automation (Playwright, FlaUI), Blue Prism Work HQ queue architectures, and integrations with cloud AI platforms. Implement prompt engineering, data extraction/OCR, database-backed transactional logging, and enterprise governance for hybrid AI-human workflows.

The Industry AI Consultant Engineer will design and implement AI solutions, lead workshops to define use cases, and collaborate with cross-functional teams to deliver AI products.

Design, build, and ship customer-facing LLM features and agentic workflows end-to-end. Implement RAG systems (chunking, embeddings, retrieval, re-ranking), safe production deployment, evaluation pipelines, drift detection, and observability. Advise on retrieval architecture, drive Context Development Lifecycle practices, and embed as a hands-on individual contributor to raise squad-wide AI quality and adoption.

Design, develop, and deploy Python-based backend services on AWS; build event-driven, high-availability systems using Step Functions, Lambda, API Gateway, DynamoDB/Aurora/Postgres, S3; integrate LLMs via AWS Bedrock and RAG frameworks; use Docker/Kubernetes/EKS and Kafka; implement observability with CloudWatch/X-Ray; collaborate with cross-functional teams to deliver AI-powered production systems.

Design, develop, and maintain Java backend applications and REST APIs; integrate AI capabilities and agent-based workflows; collaborate with engineers and AI specialists to deliver scalable, production-ready solutions; contribute to CI/CD and use AI-assisted tools to improve development quality and productivity.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Design, develop, and deploy ML models (classification, regression, NLP, CV, time-series). Prepare and feature-engineer data, build MLOps pipelines, containerize and serve models, monitor production performance, and collaborate with cross-functional teams to translate business problems into AI solutions while staying current on research and best practices.

Design, build, and operate cloud-native full-stack web applications for healthcare SaaS. Integrate LLMs and agentic AI features (retrieval, tool use, workflows). Own reliability, security, DevOps, and collaborate with product, UX and architects to deliver AI-driven clinician and patient value.

Lead end-to-end AI solution discovery, rapid prototyping, and full-stack development (LLM orchestration, RAG, agent workflows, REST APIs, UI). Partner with stakeholders to define MVPs, iterate to production with responsible AI practices (bias review, explainability, HIPAA, monitoring), and document reusable patterns for scale within healthcare engagements.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Build and own AI/LLM-assisted product features in Ruby on Rails: integrate LLM APIs, design robust backend systems with tests, fallbacks, and safeguards, improve reliability/latency/cost/security, and debug production issues.

The Head of Performance will lead performance marketing strategies, focusing on optimizing paid acquisition channels and utilizing AI tools for data-driven decisions.

Lead QA for AI platforms by defining test strategy, automation, and quality governance. Design AI-specific and CI/CD-integrated test frameworks, validate agent behavior, manage QA team, enforce quality gates, drive metrics and continuous improvement across engineering and product teams.

Design, build, and maintain demand forecasting models (classical, ML, and deep learning), implement hierarchical reconciliation, run back-testing/A-B experiments, produce explainable dashboards, and optimize model inference within an international team to inform procurement, logistics, and finance.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Design, build, and deploy production-grade agentic architectures and AI integrations. Implement MCP servers, RAG and memory pipelines, vector DBs, and enterprise-grade Java microservices with observability, guardrails, and multi-agent workflows.

Lead AI-native, spec-driven feature development using agentic coding tools. Own end-to-end delivery from executable SDD/BDD specs through production, review AI-generated code for correctness and security, maintain automated tests and CI/CD, mentor engineers, and drive adoption of AI-first development practices across distributed teams.

Lead and grow a small engineering team building and operating an end-to-end AI product: model serving, inference infrastructure, GPU capacity, deployment/versioning, retrieval and embedding pipelines, monitoring, and quality evaluation. Balance hands-on engineering (~50%) with architecture, cross-functional collaboration, and technical leadership to deliver reliable, cost-efficient AI systems.

Design, build, test, deploy, and maintain scalable web applications using Python/FastAPI and React/Node.js. Use AI coding agents (e.g., Claude Code) to accelerate development while ensuring code quality. Implement REST/GraphQL APIs, work with SQL/NoSQL databases, message queues, CI/CD, observability, and cloud deployments. Write automated tests, participate in code reviews, support production troubleshooting, and collaborate within an Agile Scrum team.