AI Engineer

• Design, build, and ship AI-powered applications end-to-end — spanning backend, data, APIs, and lightweight UI
• Establish the foundation of the Enterprise AI Platform on Azure
• Turn ambiguous business problems across multiple teams into working solutions using AI-native development tools
• Build and scale the AI Platform on Azure (multi-region)
• Own core components: APIM, identity, networking, observability, data
• Establish CI/CD, IaC, testing, and deployment standards
• Integrate with internal and third-party systems (CRM, CX platforms)
• Work with tools like Zendesk, Genesys, Intercom, Microsoft 365
• Build APIs, webhooks, and event-driven systems
• Embed GDPR, ISO 27001, SOC 2 (and others) into the platform
• Partner with Security/GRC on data protection and access control
• Partner with Product on roadmap and build-vs-buy decisions
• Document systems, architecture, and processes
• Monitor production systems and improve performance, cost, and reliability

Full Stack AI Engineer developing AI applications and platforms at Otonomee

Full Stack AI Engineer

Full Stack AI Engineer at Otonomee building AI-powered applications and platforms. Collaborating with teams to establish enterprise AI capabilities using Azure.

• Provides the most complex financial analysis for a financial function or a business, typically exercising a unique understanding of the business to determine the best method for achieving objectives.
• Consults with business leaders to guide and influence long-term and strategic decision-making within the broadest scope.
• Forecasts financial results within the broadest scope.
• Leads and advises on the most complex business initiatives, typically leading several analysts across multiple areas within finance and across geographies/organizations.
• Drives process improvement and policy development initiatives that impact the function globally.
• Represents the business unit during cross-regional or cross-functional planning processes.

Financial Lead driving complex financial analysis and strategy at HPE

HPC & AI Finance Lead

Finance Lead at HPE focusing on strategic financial analysis to support business initiatives. Collaborating with leaders to drive forecasting and ensure adherence to financial principles

• Design, develop, and deliver AI-powered features end-to-end: from proof of concept to production on our partner's stack (Angular, Node/TypeScript, PostgreSQL, Azure)
• Evaluate and integrate AI/ML services, APIs, and open-source models to solve real customer problems (document processing, root-cause analysis, ERP/MES integrations)
• Shape the AI/ML tech stack with real ownership over tools, frameworks, and architecture
• Build and maintain evaluation frameworks to ensure AI outputs meet quality, reliability, and safety standards
• Collaborate with product and engineering teams to identify high-impact AI opportunities and grow AI competency across the team

Software Engineer focusing on AI development in client-facing applications

Software Engineer – AI Focus, German Required

Software Engineer focusing on AI development in customer-centric applications for a SaaS platform using NodeJS and typeScript. Join a cross-functional team to empower operations with an AI Assistant.

• Own internal tools end to end - from understanding the operational problem through design, implementation, deployment, and iteration.
• Build across the stack as the product requires: Python/Go services on Cloud Run, TypeScript web apps and internal admin tools, event-driven integrations on Pub/Sub, and cloud infrastructure on GCP.
• Integrate with third-party SaaS platforms and cloud APIs - handling REST, webhooks, OAuth, and event-driven patterns, including failures, retries, and schema drift.
• Build and maintain workflow automation and business process orchestration as named, observable flows rather than logic buried in vendor scripts.
• Use AI tooling effectively across the development lifecycle - spec, code, and review - directing Claude as a coding collaborator while owning the output.
• Review AI-generated code before it ships for correctness, security, and type safety.
• Operate what you ship: monitoring, reliability, cost, and failure modes in production.
• Contribute to shared engineering standards, playbooks, and internal tooling.

AI Applied Engineer managing internal tools and integrations for DoiT

Applied AI Engineer – Romania

AI Applied Engineer developing integrations and internal tools for DoiT's cloud platform. Engaging with AI throughout the software development lifecycle in a remote role based in Romania.

• Drive the architecture, development and optimization of (generative) AI applications with a strong focus on model evaluation, retrieval-augmented generation (RAG), and prompt engineering.
• Implement end-to-end RAG pipelines (ingestion, chunking, embeddings, retrieval, re-ranking) and integrate them with internal data sources.
• Design and integrate data storage for AI applications using vector search in Snowflake and relational databases such as PostgreSQL.
• Analyze and integrate data from various sources, ensuring high data quality and preparing it for effective AIdriven solutions.
• Experiment with LLM models (e.g. OpenAI, Anthropic, Azure OpenAI) to improve performance, reliability and applicability across business use cases.
• Establish and maintain robust engineering practices for GenAI applications (testing, observability, security, performance optimization).
• Collaborate closely with our AI platform team to enhance, standardize and scale AI-driven capabilities across the BMW Group.
• Work closely with different business units, helping them leverage AI models for real-world applications and continuous improvements.
• Share your expertise in Generative AI, LLM orchestration, and software engineering with other teams, contributing to knowledge-sharing and innovation within the organization.

AI Engineer driving generative AI applications at Qualysoft

AI Engineer – Automotive

AI Engineer developing generative AI applications, focusing on model evaluation and RAG at Qualysoft. Collaborating with teams to leverage AI for various business applications.

• Develop and maintain AI solutions using both Machine Learning and Generative AI approaches, aligned with business and client needs.
• Build and improve LLM-based applications, including prompt engineering, orchestration flows, and tool usage.
• Implement and optimize RAG pipelines, including chunking, embeddings, ranking, and retrieval strategies.
• Work with vector databases such as pgvector, FAISS, Pinecone, or Weaviate to support semantic search and context-aware responses.
• Develop integrations using MCP or similar approaches to connect AI systems with external tools, APIs, and enterprise platforms.
• Train, evaluate, and deploy ML models for prediction, classification, clustering, or other data-driven use cases.
• Contribute to API development and integration of AI capabilities into existing applications and workflows.
• Support deployment, monitoring, and optimization of AI services in cloud and/or on-premise environments.
• Implement basic guardrails, validation, and monitoring mechanisms to improve quality, reliability, and safe usage of AI systems.
• Deploy, configure, and support AI workloads in Azure, AWS, or GCP, using cloud services for compute, storage, networking, security, and monitoring.
• Collaborate with cross-functional teams including software engineers, data scientists, and business stakeholders to deliver production-ready solutions.
• Stay up to date with relevant developments in AI, ML, and GenAI, and contribute to knowledge sharing within the team.

AI Engineer developing ML and Generative AI solutions at Accesa.

AI Engineer developing production-ready solutions for Machine Learning and Generative AI at Accesa. Collaborating with cross-functional teams to build AI systems aligned with business needs.

Machine Learning Engineer jobs in Romania