Top Artificial Intelligence Jobs in Romania

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.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Seeking a Senior Data Scientist to apply analytical methods, manage data architecture, and communicate insights to influence business decisions. Requires expertise in SQL and Python, alongside familiarity with AI tools.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.

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.

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.

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.

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 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.

Manage end-to-end global recruitment using AI to source, screen, interview, and onboard candidates. Maintain ATS workflows, recruitment documentation, KPIs, and interview coordination. Partner with leadership to improve hiring quality, reduce time-to-hire, enhance candidate experience, and build scalable, AI-first people operations and SOPs.

Use AI-augmented workflows to create marketing campaigns, websites, videos, social content, presentations, and digital assets. Maintain brand quality, prototype and iterate with emerging AI tools, collaborate across teams, and manage multiple projects to improve engagement, conversions, and creative systems.

Partner with executives to identify, prioritize, and implement AI opportunities across the organization. Develop AI roadmaps, evaluate technologies and vendors, redesign workflows, increase automation maturity, measure outcomes, establish AI governance, and drive business transformation to improve customer experience, productivity, and decision-making.

Lead multiple concurrent HealthTech, AI education, and eMarketing projects from planning through delivery. Build timelines, manage risks, coordinate cross-functional teams, run event and promotions calendars, optimize workflows using AI, maintain communication and documentation, and drive continuous operational improvement to ensure high-quality launches and stakeholder alignment.

Own the full sales lifecycle for AI-powered products: prospecting, discovery, proposals, negotiation, closing, onboarding, and account growth. Maintain CRM, track pipeline and forecasts, collaborate with marketing/product/operations, and use AI tools to improve productivity and customer outcomes.

Lead and scale demand generation by building signal-driven, automated GTM systems using AI and no-code tools. Own MQL-to-pipeline metrics, optimize CAC, run performance campaigns, integrate CRM and enrichment workflows, collaborate with sales/RevOps to improve conversions and pipeline ROI.

Lead engineering to shift GTM from no-code automation to code-first, AI-agent-driven systems. Design scalable backend pipelines, integrate CRMs/APIs, build reusable agent frameworks, ensure reliability and observability, and hire/mentor a distributed engineering team to deliver production GTM infrastructure.

Remote SDR responsible for outbound prospecting, qualifying leads, booking demos, maintaining CRM records, collaborating with marketing and sales, and meeting monthly KPIs in the AI SaaS space.

Lead development of scalable AI-powered GTM systems, migrating from no-code to Claude Code agent architectures. Build integrations, data pipelines, and reusable agent frameworks; ensure reliability, observability, and cost optimization. Hire, mentor, and manage a distributed engineering team while evaluating emerging AI tools and driving platform innovation.

Drive the full sales cycle for AI automation solutions: outbound prospecting, discovery calls, objection handling, positioning demos, and closing high-ticket deals. Maintain CRM records, use prospecting tools, and collaborate with leadership to refine strategy and hit revenue targets.

Build, launch, and optimize high-performance outbound campaigns and automated GTM systems using Clay, Claude, SmartLead, HeyReach, n8n, and APIs. Translate buying signals into targeted prospecting workflows, iterate on messaging and sequences, track CRM attribution, and optimize for pipeline outcomes and campaign KPIs (launch velocity, conversion, pipeline contribution).

Manage and deploy AI-driven automation and workflows, onboard clients, translate requirements into actionable automations, design prompts for LLMs, troubleshoot and iterate systems, coordinate with AI engineers, and track project timelines and performance.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.