Top Artificial Intelligence Jobs in Romania

Build and scale low-latency AI serving infrastructure for SLMs, prompt-injection engines, and classification services. Architect GPU-accelerated inference pipelines, backend microservices, Kubernetes clusters, rate limiting, tenant guardrails, monitoring, and autoscaling systems. Develop continuous evaluation, retraining, model CI/CD, regression gates, and MLOps/LLMOps pipelines while improving service reliability, latency, cost efficiency, and detection quality.

Build AI-enhanced customer engagement tools and applications using modern cloud technologies. Develop prototypes and proofs of concept with frameworks such as Angular, TypeScript, JavaScript, LangChain, and LangGraph. Contribute to SAP Fiori and CAP-based solutions on Node.js and SAP Business Technology Platform, collaborate in Scrum teams, take ownership of tasks, and support automated testing and reliable production releases.

Own the production architecture of an AI-assisted KYC and customer due diligence platform across Azure and Databricks. Responsibilities include designing networking, identity, environments, integrations, resilience, monitoring, backup and rollback; defining operating models and support processes; planning service transition; and representing the solution to architecture boards and senior stakeholders.

Own cloud infrastructure, CI/CD pipelines, observability, incident response, recovery procedures, and reliability automation across applications and AI agents. The role uses Infrastructure as Code, scripting, monitoring, SLOs, and AI-assisted operations to improve platform resilience and reduce toil.

Build and maintain automated API, integration, and end-to-end tests for AI agents and critical workflows. Evaluate agent task completion, response quality, tool usage, permissions, and changes to models, prompts, and context. Integrate quality checks into CI/CD, convert production defects into regression tests, use AI to explore workflows and reproduce bugs, and share testing methods with AI and QA teams.

Build and deploy AI-powered products across the frontend, backend, APIs, databases, and model integrations. Own features end to end, design maintainable interfaces and data models, connect applications to LLMs and agents, use AI-assisted coding tools, write tests, review code, troubleshoot production systems, and mentor teammates.

Own access, identity, and integration work connecting the AI platform to business systems. Translate requests into API, authentication, account, scope, and permission requirements; coordinate IAM, SSO, OAuth apps, approvals, connector builds, testing, credential rotation, revocation, documentation, and troubleshooting across IT and engineering teams.

Lead the engineering function on a remote consulting mission by setting technical direction, managing and mentoring engineers, driving the roadmap, overseeing complex delivery, improving engineering practices, and partnering with product and business stakeholders. The role requires hands-on technical leadership, architectural judgment, agile expertise, and evaluation of emerging technologies including cloud, AI, and blockchain.

Lead the engineering function for a growing technology company on a renewable consulting mission. Responsibilities include setting the engineering roadmap, managing and mentoring engineers, guiding architecture and technical decisions, leading complex projects, improving engineering practices, and collaborating with product and business stakeholders. The role requires hands-on technical involvement, strong software engineering expertise, and the ability to evaluate emerging technologies including cloud, AI, and blockchain. French fluency and strong English communication skills are mandatory.

Staff AI Engineer in dLocal’s AI Lab responsible for scouting, prototyping, benchmarking, and evaluating emerging AI technologies. Builds evaluation harnesses and experimental systems for LLMs, agents, retrieval, and orchestration; assesses quality, cost, latency, security, and reliability; and produces adoption recommendations. Defines guardrails and readiness guidance, coordinates production hand-offs, supports governance, mentors engineers, and communicates findings to technical and non-technical stakeholders.

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 and delivers agentic AI and RAG solutions using Azure AI, Python, TypeScript, orchestration frameworks, and cloud infrastructure. Responsibilities include architecture, data ingestion, backend and API development, deployment, monitoring, AI governance, guardrails, evaluation, and client guidance. The role also prototypes solutions, leads technical projects, and supports digital transformation initiatives.

Lead end-to-end product backlog for procurement interoperability and orchestration across source-to-pay. Design integration and data alignment across procurement platforms, run stakeholder workshops, translate demand into user stories and releases, manage E2E testing, rollout and BAU transition, and embed AI-driven automation and prompt-driven workflows to improve efficiency.

Own the direction and execution of AI products across RemotePass’s payroll, compliance, payments, and operations platform. Identify high-value automation opportunities, determine appropriate human oversight, translate customer problems into product decisions, and balance speed, accuracy, and trust. Partner closely with engineering, executives, and operators to define strategy, write specifications, run experiments, ship features, and scale successful AI capabilities from early concepts into production.

Build and deploy production recommendation, ranking, personalization, promotion, and customer segmentation systems. Own end-to-end machine learning pipelines from feature preparation through deployment and monitoring, using BigQuery or Snowflake and Airflow. Develop real-time models across web, app, and in-store touchpoints, and evaluate business impact through A/B testing. Collaborate internationally while applying MLOps, experimentation, and model validation practices.

Leads governance and responsible deployment of Salesforce Agentforce and Einstein capabilities. Defines AI policies, risk assessments, guardrails, monitoring, audit practices, and compliance processes. Advises teams on agent design, grounding, prompts, human handoff, and production readiness while partnering with security, legal, data, architecture, and compliance stakeholders. Develops enablement materials, metrics, and continuous-improvement practices for enterprise AI adoption.

Build and operate production AI products, including sales acceleration systems, recommendation engines, and agentic operating systems. Own solutions end to end by gathering user needs, designing, coding, deploying, monitoring, and maintaining them. Evaluate agent decisions, detect performance drift, enable non-engineers to create automations, and contribute to shared AI infrastructure. Work across TypeScript, Rust, Ruby, Google Cloud, Kubernetes, and infrastructure as code while collaborating across advisory, sales, operations, and engineering.

Designs and delivers AI-native solutions across business functions including People, Legal, Finance, and Operations. Responsibilities include identifying transformation opportunities, building and deploying AI agents and automated workflows, integrating enterprise systems, driving adoption, measuring business impact, applying governance and human oversight, and mentoring colleagues. The role partners with functional leaders to redesign operating models and continuously improve production AI solutions.

Build and operate secure, scalable payment services and third-party integrations while developing AI-powered and agentic workflows. Responsibilities include payment processing, reconciliation, settlement, tool calling, retrieval, permissions, guardrails, observability, testing, performance optimization, and technical architecture. The role requires Golang, production software engineering, distributed systems, databases, REST APIs, payment or POS integrations, LLM applications, and secure AI development practices.

Build and operate production Generative AI applications and reusable services for digital experiences. Responsibilities include RAG, semantic search, conversational and agentic applications, content ingestion, evaluation, safety guardrails, APIs, AWS cloud infrastructure, observability, CI/CD, and AEM website integration. The role partners across product, UX, content, architecture, analytics, security, and engineering teams to deliver scalable, secure, personalized AI experiences.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.

Architects and delivers enterprise AI, machine learning, generative AI, RAG, and agentic systems. Designs secure end-to-end architectures, integrates Claude and other models with ServiceNow, APIs, databases, cloud platforms, and workflows, and leads discovery, proofs of concept, and client delivery. Develops ML and NLP solutions, data pipelines, dashboards, evaluation frameworks, observability, governance, and responsible AI controls. Collaborates with stakeholders, engineers, consultants, and clients to operationalize reliable AI applications.

Build and scale secure, production-grade internal AI platforms, agents, MCP integrations, automation tools, and multi-agent workflows. Design cloud infrastructure, integrations, governance, security controls, and cost optimization systems for organization-wide AI adoption. Partner with engineering and business teams, establish AI standards, measure productivity impact, and support adoption across an international organization.

Owns quality verification for AI-assisted migration of legacy desktop financial applications to modern web solutions. Responsibilities include designing test strategies, defining traceable behavioral specifications, developing automated quality gates, testing REST APIs and web front ends, validating data against legacy systems, measuring test effectiveness, conducting acceptance testing, and delivering knowledge transfer to client engineering teams.