Generative AI Engineer The Generative AI Engineer designs, builds, evaluates, and deploys production-ready AI-powered applications. This role combines applied machine learning, generative AI, backend engineering, and cloud deployment skills to turn AI capabilities into scalable, secure, and maintainable business solutions. The role focuses on large language models, retrieval-augmented generation, AI agents, workflow automation, APIs, and enterprise AI services. Your Mission Build AI-powered applications using Generative AI, Machine Learning, and LLM-based technologies. Design and implement RAG solutions, prompt-based systems, AI agents, embeddings, and semantic search. Integrate LLM APIs, open-source models, and AI components into enterprise applications. Develop backend services, REST APIs, microservices, and model-serving endpoints using Python and/or .NET C#. Work with structured and unstructured data across SQL, NoSQL, object storage, data lakes, and vector databases. Build data pipelines for ingestion, preprocessing, retrieval, and AI interaction. Evaluate AI systems for quality, relevance, hallucination, latency, cost, reliability, and security. Apply MLOps and LLMOps practices, including CI/CD, monitoring, evaluation, versioning, and feedback loops. Deploy AI services to cloud environments, especially Azure, using containers and orchestration platforms. Collaborate with product, engineering, data, architecture, security, and business teams. Ensure solutions follow responsible AI, privacy, security, scalability, and maintainability standards. Strong proficiency in Python for Generative AI, Machine Learning, data processing, and backend development. Experience with backend development using .NET C# and/or Python frameworks such as FastAPI. Hands-on experience building REST APIs, microservices, and production-grade backend services. Practical experience with LLM APIs, prompt engineering, embeddings, RAG, and semantic search. Understanding of NLP concepts, language models, text processing, and AI evaluation. Experience with SQL, NoSQL, object storage, data lakes, and vector databases. Experience deploying applications or AI services to Azure or similar cloud platforms. Familiarity with Docker, Kubernetes, CI/CD, monitoring, and observability. Understanding of MLOps or LLMOps practices. Ability to optimize AI applications for quality, performance, latency, scalability, and cost. Knowledge of secure software development practices for APIs, cloud services, and enterprise systems. Benefits Attractive and transparent remuneration. Hybrid work model and flexible working hours. 22 days of vacation per year. Health insurance. Access to online learning platforms and internal academy. Integration into a dynamic, enthusiastic, and constantly growing team. #J-18808-Ljbffr