Top Artificial Intelligence Jobs in Bucharest

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.

Architect and build production AI-powered HR systems using agentic workflows, multi-agent systems, APIs, and workflow automation. Integrate Workday and other HR tools, implement governance, audit logging, privacy controls, and human approval gates, and ensure reliable payroll-adjacent workflows. Document solutions, train People team users, drive adoption, and measure automation impact. The role requires strong software engineering, AI application development, HR technology, Workday, data privacy, and workflow automation experience.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Design and deploy production-grade AI solutions to improve automotive inventory discoverability, generate scalable vehicle content, cluster search intent, optimize ranking signals, identify high-potential inventory combinations, measure impact via experimentation, and collaborate with product and engineering teams to operationalize SEO and GEO initiatives.

Maintain and enhance SE Power BI dashboards, validate data integrity, support Salesforce (including Vivun) triage and basic admin, prepare data for quota planning, document processes, and apply AI-assisted tools to streamline reporting and operational workflows.

Design and maintain CI/CD, repository governance, secrets and access management, observability, and deployment automation for .NET-based AI agents. Collaborate with AI engineers using Claude/OpenAI, ensure security, compliance (GDPR), operational readiness, and support multiple development teams to move AI agent solutions into production.

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 and build LLM-powered agents to automate cybersecurity workflows within the Secure Software Development Life Cycle, with priority on vulnerability detection and remediation. Prototype, evaluate, and integrate agentic tools with SSDLC tooling, collaborate in Agile ceremonies, and share best practices on LLMs, agentic patterns, and cloud delivery.

Advise organizations on enterprise data and AI strategy, conduct maturity assessments, define governance and operating models, prioritize BI/ML/GenAI/NLP use cases, design AI-ready data platform roadmaps, and present recommendations to executive stakeholders.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Invite software agencies and engineers to connect Git repositories for evaluation and potential licensing for AI training. Platform measures source-code and Git-history token volume, provides estimated licensing value and quality feedback, and pays contributors when repositories are used under license. Must own or have authority to license code and meet platform quality and legal requirements.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

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.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

Lead architecture and hands-on development of an AI-native underwriting platform. Design scalable backend services in Python, integrate with React/TypeScript frontends, promote engineering standards, implement observability and security, and use AI-assisted development tools to improve productivity while mentoring the team and driving the platform from MVP to production.

As an AI-native growth hacker, you will implement a growth strategy, focusing on a specific growth wedge, leveraging AI tools, and measuring performance to scale successfully.

Design, build, and run production agentic workflows and reusable components for delivery processes. Implement RAG-backed knowledge bases, monitoring, evals, observability, and human-in-the-loop feedback to improve agents and integrate them with delivery teams and business tools.

Join the MLOps team to deploy, monitor and optimize machine learning models in production. Build data and model pipelines, implement CI/CD, ensure model performance and scalability, and collaborate with cross-functional teams to improve infrastructure and delivery.

Design, build and deploy autonomous AI agents and orchestration workflows to automate complex business processes. Lead PoCs with LLMs, integrate agents with enterprise systems, set governance and best practices, and upskill teams to adopt agentic automation.

Lead design, development and deployment of scalable AI/LLM systems (RAG, agentic systems). Architect end-to-end ML pipelines, apply MLOps/LLMOps, integrate AI with enterprise platforms, ensure security/privacy/compliance, mentor teams, and drive presales and delivery for enterprise clients.