Top Artificial Intelligence Jobs in Bucharest

Create short films and videos using AI photo and video generation tools. Edit AI-generated content, develop concepts with marketing teams, maintain brand alignment, experiment with creative formats, and deliver engaging content for Tether and affiliated brands. The role requires video production and editing expertise, strong storytelling and visual composition skills, familiarity with digital marketing and social media, and the ability to manage multiple projects independently under tight deadlines.

Build and operate production AI services, including multi-agent systems, LLM pipelines, MCP servers, RAG and document-ingestion workflows. Develop reliable Python backend services, evaluate model quality, manage deployments, observability, authentication and privacy safeguards. Collaborate with engineering and product teams while supporting AI governance, risk documentation and ISO evidence. The role focuses on practical business automation across document processing, customer support, CRM and account operations.

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.

Develops World Vision International’s policy positions and external influence on children’s digital rights, AI governance, online safety, and digital participation. The role analyzes global regulatory developments, produces thought leadership and advocacy content, represents the organization in policy forums, leads coalition engagement, and advises internal teams and senior leaders. It requires collaboration across international teams and travel of up to 25%.

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.

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.

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.

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Develop and maintain AI and machine learning models, contribute to projects from scoping through release, optimize prompts, and support system performance and reliability. Collaborate with cross-functional teams and experienced engineers to deliver business-focused AI solutions. The role involves LLMs, generative AI, cloud platforms, experimentation, and emerging techniques such as retrieval-augmented generation.

Leads QA automation strategy across web, API, and backend services. Designs and maintains automated test frameworks, integrates testing into CI/CD pipelines, investigates defects, maintains test environments, and promotes quality practices. Mentors junior engineers and collaborates with product and engineering teams on acceptance criteria and release quality. Applies AI-assisted tools for test generation, debugging, defect analysis, and automation optimization.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Analyzes channel and business processes, identifies inefficiencies, gathers cross-functional requirements, and helps implement automation, AI, and integrated technology solutions. Conducts data analysis, facilitates stakeholder meetings, monitors solution effectiveness, and recommends process improvements. The role supports Channel growth within Honeywell’s Industrial Automation business and requires collaboration across systems, data, and functional teams.

Own daily operations and quality assurance for an internal AI-powered growth platform. Test releases, rule changes, edge cases, and regressions; document and verify bug fixes; monitor platform and agent activity; manage approval queues; evaluate agent output; escalate risks; and maintain test plans, QA checklists, runbooks, and release notes.

Own HR support across recruitment, employee and manager advisory, HR projects, organizational transformations, and AI enablement. Responsibilities include sourcing and interviewing candidates, supporting talent reviews and pay transparency initiatives, improving HR processes, advising managers on people matters, and representing HR leadership when needed. The role is fully remote and requires independent management of varied, ambiguous work.

Optimize LLM inference performance on modern GPUs by analyzing GPU architecture, memory access, kernels, and bottlenecks. Develop, debug, and reverse-engineer CUDA kernels; optimize matrix multiplication, attention, and neural network operations; benchmark latency, throughput, and utilization; and work with inference frameworks such as vLLM, SGLang, TensorRT-LLM, Triton, and FlashAttention.

Provide technical leadership for agentic and LLM-based AI initiatives from problem definition through production. Design architectures involving orchestration, retrieval, tool use, state management, and external integrations. Establish evaluation, testing, observability, reliability, security, governance, and cost-management practices. Lead cross-functional initiatives, define reusable engineering standards, mentor engineers and technical leads, and communicate technical decisions to business and engineering stakeholders.

Designs, develops, and deploys secure Power Platform applications, automated workflows, and AI-powered assistants. Builds solutions using Power Apps, Power Automate, Dataverse, Copilot Studio, LLMs, RAG, agents, embeddings, and vector databases. Manages integrations, CI/CD, application lifecycle management, testing, governance, security, and enterprise data connections. Advises customers and colleagues on prompt engineering, responsible AI, and automation while mentoring team members and ensuring scalable, compliant solutions.

Lead a squad of 8–20 engineers while remaining hands-on in frontend, backend, cloud, and AI development. Design domain-driven, event-driven architectures; build LLM-powered applications using Claude, AWS Bedrock, and LangGraph; translate Figma designs into accessible interfaces; establish engineering standards; manage CI/CD with GitHub Actions and AWS CDK; implement automated testing; and mentor engineers during agile delivery.

Designs, builds, and supports enterprise AI agent solutions using Microsoft Copilot Studio, Microsoft 365, Azure, and Power Platform. Leads customer engagements, resolves technical blockers, improves architectures, drives adoption, manages delivery through a factory model, and advises technical and business executives. Collaborates with partners and field teams, develops reusable AI patterns, influences product improvements, and mentors others while ensuring solutions are secure, scalable, reliable, and aligned with customer business outcomes.