Top Artificial Intelligence Jobs in Romania

Lead research and production development of LLMs and transformer models for machine translation and language quality. Design evaluation frameworks, agentic NLP workflows, training pipelines, and model-provider strategies. Own flagship model programs from data curation through production integration, mentor researchers, review technical work, and collaborate with Product and Engineering to ship capabilities. The role requires deep NLP and LLM expertise, rigorous experimentation, strong Python and ML library skills, and technical leadership.

Lead 0-to-1 product development for Kimchi’s AI coding and infrastructure products. Responsibilities include identifying high-value customer problems, conducting discovery, validating hypotheses, prioritizing opportunities, partnering with engineering and design, defining success metrics, driving adoption, and communicating product direction. The role requires strong judgment in ambiguous, rapidly changing technical markets and hands-on use of modern AI tools.

Lead engineering execution and technical direction while remaining approximately 75% hands-on. Responsibilities include developing LLM- and AI-powered products, coding, code review, debugging, platform performance, sprint planning, cross-functional coordination, technical mentorship, recruiting, and supporting engineers’ growth. The role partners closely with founders and product teams and offers a path toward Engineering Manager responsibilities.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Designs enterprise-grade cloud, data, and AI solutions for airline operations. Responsibilities include assessing technology landscapes, identifying modernization and integration opportunities, translating business priorities into technical architectures, creating solution blueprints and documentation, promoting enterprise architecture best practices, and guiding customer teams through transformation initiatives.

Design and lead multi-domain cloud, data, and AI architectures for aviation operations. Translate business needs into scalable technical solutions, guide technology decisions, oversee platform integration, and provide technical leadership and mentorship to customer development teams. Collaborate with technical and business stakeholders while balancing performance, security, scalability, and cost.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive intelligence, sales enablement, technical content, and cross-functional collaboration. The role targets enterprise buyers through sales-led motions and requires measurable pipeline impact, strong writing and presentation skills, and comfort working with technical teams. Experience in AI, contact centers, QA, testing, observability, or monitoring is advantageous.

Produces daily 60–90 second AI-generated micro-drama episodes for TikTok, Instagram Reels, and YouTube Shorts. Responsibilities include developing striking visuals, consistent characters, natural voices, audio, purposeful editing, and story-driven visual styles. The role requires prompt engineering, strong editing fundamentals, independent execution, fast delivery, and familiarity with vertical video. Experience in traditional video production, dramaturgy, end-to-end AI video creation, and short-form drama is advantageous.

Design, prototype, deploy, and maintain AI-powered internal tools and automation for business teams. Build full-stack applications, agents, workflows, enterprise integrations, and reusable components using generative AI and cloud technologies. Ensure security, governance, testing, observability, documentation, and measurable business impact while supporting adoption across Sales, Support, Product, Operations, and other teams.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Creates technical documentation, implementation guides, whitepapers, product content, landing pages, and blog posts covering AI infrastructure, GPU computing, cloud infrastructure, enterprise hardware, cybersecurity, and related technologies. Researches complex technical subjects, collaborates with technical experts, validates accuracy, and translates specifications and architecture concepts into clear content for engineers, IT professionals, architects, and technology decision-makers.

Own a portfolio of B2B customer accounts from onboarding through adoption, renewal, and expansion. Configure AI agent call flows, prompts, integrations, transfer rules, and notifications; launch and test customer implementations; manage escalations and follow-ups; communicate proactively by phone and email; identify churn risks, collect past-due revenue, close upsells, document recurring fixes, and surface new automation opportunities.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Design and maintain scalable data pipelines and platforms for industrial and operational data. Integrate legacy systems, IoT, time-series data, and technical documentation while implementing cloud data architectures, governance, metadata, quality, and lineage practices. Prepare curated datasets for analytics, predictive maintenance, digital twins, machine learning, and AI use cases. Support Power BI dashboards and collaborate with data scientists, engineers, and domain experts.

Build Python-based backend services, APIs, and integrations that embed AI capabilities into a SaaS platform. Develop scalable customer-facing features, connect AI outputs to workflows and dashboards, collaborate with product, design, and platform teams, and implement observability, monitoring, and error handling. Contribute to the AI integration stack, including SDKs, MCP servers, and LLM orchestration.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

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.

Build and shape GenAI solutions using AI agents, LLMs, Python, Databricks, and lakehouse architectures. Responsibilities include agent orchestration, data engineering, knowledge modeling, ontology development, web data extraction, and integration with Databricks capabilities. The role owns technical architecture and delivery, evaluates evolving tools and frameworks, develops production-quality software, and collaborates on design decisions and continuous improvement.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

Manage global data privacy and AI compliance, including GDPR documentation, contracts, DPIAs, AI risk classification, regulatory monitoring, responsible AI policies, internal audits, and cross-jurisdictional advisory support. The role partners with legal, product, and technology teams to implement privacy and AI governance requirements across EU, LATAM, APAC, and MEA markets.

Analyze marketing and business workflows, identify opportunities for AI automation, and design, implement, document, and improve AI-supported processes. Evaluate AI tools and integrations, monitor performance and adoption, conduct business and market analysis, support change management, train colleagues, and communicate recommendations to stakeholders. The role connects business requirements with practical AI-enabled execution without requiring production software development.

Designs and deploys production-grade AI software, including agentic systems, generative AI pipelines, secure microservices, REST APIs, and RAG applications. Responsibilities include Kubernetes and CI/CD management, containerized deployments, GPU and CUDA optimization, technical architecture, effort estimation, technology scouting, mentoring, and engineering standards. The role supports security and intelligence projects involving machine learning, deep learning, NLP, computer vision, and multimodal AI.

Partner with stakeholders to identify and prioritize AI use cases, map current and future business processes, assess feasibility and value, recommend build/buy options, and ensure solutions meet architecture, data, security, governance, and responsible AI requirements. Act as a bridge between business, IT, data science, and delivery teams.

Own the lifecycle, backlog, roadmap, adoption, and success metrics for shared AI services used by Esko product and engineering teams. Gather and prioritize cross-functional needs, define requirements, guide teams adopting AI capabilities, communicate trade-offs, support testing and production adoption, and help shape the company’s AI strategy. The role requires software product management experience, production AI deployment expertise, and strong stakeholder communication.