Top Artificial Intelligence Jobs in Romania

Build and operate production-grade AI applications across Python backends and React front ends. Develop LLM, Generative AI, Agentic AI, and RAG solutions; integrate models, vector databases, APIs, and cloud platforms; and implement secure, scalable, observable architectures. Responsibilities include DevOps, CI/CD, testing, monitoring, technical design, code reviews, mentoring, and collaboration with multidisciplinary teams from prototyping through continuous improvement.

Lead enterprise Agentforce architecture and hands-on agentic AI engineering for European clients. Design agent workflows, configure Salesforce agents, integrate deterministic Apex and Flow logic, implement LLM orchestration, RAG, APIs, MuleSoft pipelines, security guardrails, and automated testing. Conduct technical discovery, advise enterprise stakeholders, and guide engineering teams through production deployments. This is a fractional, project-based advisory role requiring deep Salesforce architecture and advanced LLM engineering expertise.

Lead Nuitee’s agentic AI strategy and capabilities for searching, booking, paying for, modifying, and canceling hotel stays. The role combines hands-on software engineering, product development, technical leadership, commercial opportunity identification, and team building. Reporting to the CEO, this leader will design secure agent-native APIs and MCP tools, improve transaction reliability, and take new products from concept to production across Nuitee’s travel infrastructure.

Build a model enablement platform for heterogeneous AI computing across AIPUs, CPUs, GPUs, NPUs, and other accelerators. Responsibilities include model ingestion, graph partitioning, compilation, runtime execution, performance profiling, memory and scheduling optimization, large-model offloading, and verification integrated into CI. The role combines architecture ownership with hands-on C++ and Python engineering across model frontends, compilers, runtimes, and application pipelines.

Lead the AI architecture for Productboard Insights, designing agent-native systems, context infrastructure, self-healing workflows, evaluation processes, and AI code-review optimization. Own technical direction across Insights, Spark, and platform teams; drive features from concept through beta; optimize quality, latency, cost, and reliability; write production code; and develop engineers through documentation and reviews.

Assess and approve AI-enabled, cloud, and SaaS applications for security, regulatory, and compliance risks. Provide cybersecurity consulting, review architectures and assessments, develop security standards and guardrails, support audits and remediation, and guide stakeholders in securely adopting emerging technologies. Collaborate with architecture, legal, privacy, procurement, IT, and business teams across international operations.

Develop scalable, responsive front-end applications using Vue.js and TypeScript. Build accessible, reusable Storybook components, integrate REST APIs and WebSockets, optimize performance, and contribute to architecture, engineering standards, code reviews, and mentoring. Experience with mapping technologies, geospatial data, large datasets, real-time applications, accessibility, design systems, and CI/CD is valuable.

Build and own AI models, data pipelines, evaluation suites, and AI product features for construction procurement software. Develop document extraction and workflow automation using generative AI, LLMs, RAG, and optimization algorithms. Design complex data models, integrations, backend services, and production systems using Go, GraphQL, PostgreSQL, and AWS.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Owns product vision, roadmap, strategy, and prioritized backlog for sustainability automation products in Private Bank Germany. Partners with business stakeholders, architects, program managers, and engineering teams to define requirements, deliver value through Agile practices, drive adoption, and support scaled Agile transformation. Provides domain guidance on sustainability, financial services, testing standards, data products, and responsible AI use.

Leads enterprise AI and machine learning coordination, governance, prioritization, scalability assessment, and deployment. Identifies opportunities, manages the AI initiative pipeline, establishes standards and validation frameworks, advises on architecture and models, supports hands-on prototyping and troubleshooting, and drives cross-functional resource alignment. Tracks adoption, ROI, and business impact while enabling change management, training, and global AI capability development through influence in a matrixed organization.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Own the vision, roadmap, strategy, and backlog for technology products and automation across Private Bank Germany. Prioritize features, coordinate business and engineering stakeholders, elicit requirements, drive Agile delivery, support scaled Agile adoption, and guide teams across locations. The role also requires sustainability domain expertise, strong understanding of banking products and data, software development practices, testing, analytics, reporting, and responsible use of AI tools.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Build and operate production AI infrastructure powering intelligent agents at scale. Develop high-performance Rust services for model inference, orchestration, and execution; design scalable distributed systems; establish MLOps, observability, guardrails, and failure recovery; and optimize latency, throughput, reliability, and cost. Partner with agent systems and infrastructure teams to turn prototypes into hardened services serving millions of users.

Contribute to enterprise-grade full-stack web applications with generative AI capabilities. Build React and TypeScript frontend features, support Node.js or Python backend services, integrate LLM functionality, work with SQL and NoSQL databases, and assist with AWS cloud deployment. The role emphasizes clean, tested code, Agile collaboration, code reviews, mentorship, and developing proofs of concept into reliable production features.

Leads end-to-end architecture for enterprise data, analytics, and AI solutions in a Microsoft-first environment. Translates business requirements into secure, scalable designs and guides prototyping, implementation, architecture reviews, troubleshooting, governance, and production readiness. Requires deep hands-on experience with Azure data platforms, Power BI, enterprise security, Azure OpenAI, and retrieval-augmented generation patterns.

Manage influencer campaigns end to end across multiple brands and markets. Responsibilities include creator casting, briefing, negotiation, contracting, campaign timelines, budgets, approvals, client communications, performance tracking, reporting, and optimization. The role requires using CPV and campaign KPIs to manage commercial outcomes, ensuring content meets brand and compliance requirements, and operating as a power user of Kyra’s platform and AI tools.

Serve as the bridge between AI and automation strategy, business needs, and technology delivery. Identify and prioritize use cases, conduct stakeholder workshops, define requirements and value cases, support roadmaps and agile delivery, measure business outcomes, and promote responsible AI adoption. The role also supports product rollout, training, communications, KPI tracking, and quarterly reporting across the organization.

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.

Fine-tune and post-train open-weight LLMs using methods such as SFT, LoRA, QLoRA, DPO, and GRPO. Build training data and evaluations, diagnose model failures, improve personalization and multilingual behavior, accelerate reproducible training workflows, and deploy models to production. The role involves using real user behavior to guide iterations across a large-scale AI companionship platform, with collaboration across AI, product, web, DevOps, and leadership teams.

Lead technical delivery of an AI-assisted operations platform from discovery through implementation and evaluation. Establish technical baselines, validate integrations, design maintainable rules and AI workflows, guide engineers, review code, implement testing and observability, and ensure traceable human-approved decisions. The role requires hands-on architecture, API integration, evaluation, CI, production readiness, and clear communication with business and engineering stakeholders. Travel to an in-person working session in Milan is included.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship end-to-end product features, set technical direction, evaluate LLM reliability and trade-offs, and accelerate validated learning through close collaboration with engineering, product, and design teams.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship production software, establish technical direction, and partner with product and design teams to accelerate validated learning. The role requires strong backend and distributed-systems expertise, hands-on LLM product experience, and practical use of AI coding tools.

Develop and implement scalable AI and automation platforms using full-stack JavaScript and AI technologies. Build core front-end and back-end components, integrations, and customer-facing digital products. Collaborate with distributed technology and business teams to translate requirements into technical solutions, establish architecture and engineering standards, and promote best practices across global implementation teams.