Top Artificial Intelligence Jobs in Bucharest

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 design and implementation of scalable automation systems and integrations using GoHighLevel and tools like n8n/Make/Zapier. Build workflows for lead generation, nurturing, content creation, and sales pipelines; integrate CRMs with email, calendars, payments, and project tools. Maintain documentation (SOPs), advise clients, provide technical support, and own internal automation infrastructure to ensure reliability, scalability, and clear communication with non-technical stakeholders.

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.

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.

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).

Design, develop, and deploy AI/ML models and LLM-powered workflows (chatbots, copilots, automation). Integrate models with APIs and SaaS platforms, fine-tune prompts, work with structured/unstructured data, monitor performance, and collaborate with product and engineering teams to deliver scalable AI solutions.

Design, develop, and deploy production-ready AI solutions for insurance, focusing on agentic AI, multi-agent systems, LLMs, RAG pipelines, and computer vision. Research state-of-the-art methods, build scalable products and proprietary models, and take projects from architecture and PoC through production while collaborating with data science and engineering teams.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Design, build, evaluate, and deploy LLM-driven features and AI systems. Collaborate with engineering and product teams to implement RAG, monitoring, CI/CD, and scalable cloud ML deployments while continuously improving model performance.

As the AI Agents Solutions Architect at Kraken, you'll design and build an AI-native finance operating system, automating finance operations and ensuring compliance in a regulated environment.

The AI Video Editor / Motion Designer creates AI-driven videos to enhance advertising effectiveness. Responsibilities include video production, concept development, utilizing AI tools, performance analysis, and collaboration with team members.

As a Forward Deployed Engineer, you'll bridge customer needs and OPAQUE's AI platform, designing production-grade solutions and managing deployment integration.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Build and maintain enterprise web applications using .NET and Angular, participate in full SDLC, integrate AI services and APIs, leverage AI-powered development tools and LLM/RAG solutions, contribute to architecture and technical decisions, collaborate with clients and delivery teams, and ensure compliance with enterprise/regulatory standards.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Review, evaluate, and annotate AI-generated multilingual and multimodal content (text, images, audio, video). Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve datasets and annotation guidelines.

The role involves reviewing AI-generated outputs, analyzing multimedia, and providing culturally relevant content feedback. Strong analytical skills and attention to detail are required.

Design, build, and scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

Lead end-to-end development and integration of AI features (LLMs/ML) into web/mobile apps and APIs. Design AI-first UX, craft and optimize system prompts, build agentic workflows (LangChain/CrewAI/AutoGen), implement error-handling and hybrid cloud/on-prem deployments, and collaborate within product squads using Agile/Scrum.

Lead design, build, and operate a multi-tenant AI platform and LLMOps infrastructure across cloud and on-prem. Own vector DBs, feature stores, model hosting (Kubernetes + GPU orchestration), and developer self-service tooling. Standardize LLM-as-a-service offerings and enable squads with APIs, templates, and documentation.

Design, develop, fine-tune, and deploy ML and NLP models (including MT and LLM finetuning). Own projects end-to-end using Python, TensorFlow/PyTorch, Docker, MLflow, and AWS; perform statistical analysis, monitor production models, document experiments, and collaborate across teams.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.

Perform guided evaluations of filters and options on desktop and mobile to ensure relevance to customer queries. Follow guidelines, conduct online research, complete qualification tests, and maintain accuracy and consistency. Work remotely as a freelance data rater in Swedish, completing 5–15 flexible hours weekly.