20 hours ago
Budapest, HungaryMid Level / Senior
Responsibilities
- Configure, integrate, validate, deploy, and troubleshoot enterprise AI platforms across on-premises, private cloud, hybrid cloud, and public cloud environments.
- Develop hands-on AI prototypes and proof-of-concepts using LLMs, open-weight models, model APIs, embeddings, vector databases, retrieval-augmented generation, and agentic AI workflows.
- Translate customer requirements into solution concepts, architectures, sizing, technical designs, and implementation approaches.
- Integrate AI platforms with APIs, data sources, identity and authentication services, security controls, and existing business applications.
- Lead the technical delivery of proof-of-concepts and sales engagements and transition validated solutions toward secure, scalable, operable implementations.
- Create and deliver technical demonstrations and workshops for customers and internal stakeholders.
- Troubleshoot and optimize applications, AI platforms, containers, infrastructure, integrations, and model-serving environments.
- Develop reusable reference architectures, deployment patterns, configurations, documentation, runbooks, and demonstration environments.
- Collaborate with AI architects, cloud and infrastructure teams, networking, security, delivery, sales, and technology partners.
- Evaluate emerging enterprise AI platforms, frameworks, model-serving technologies, and cloud-native capabilities.
Requirements
- 3–5 years of hands-on IT experience in software engineering, DevOps, cloud engineering, platform engineering, solution engineering, systems integration, or a comparable technical role.
- Strong practical experience with Linux, Python, REST APIs, Git, scripting, and technical troubleshooting.
- Hands-on experience with containers and deployment technologies, particularly Docker, plus working knowledge of Kubernetes or comparable orchestration platforms.
- OpenShift experience is a strong advantage.
- Practical understanding of generative AI, including LLM APIs or model inference, embeddings, vector search, retrieval-augmented generation, and agentic AI concepts.
- Experience integrating and deploying software across on-premises, private cloud, hybrid cloud, or public cloud environments such as Azure, AWS, or Google Cloud.
- Understanding of model serving, inference, GPU-based workloads, security, identity, networking, monitoring, and production-readiness considerations.
- Customer-facing experience with pre-sales, technical workshops, proof-of-concepts, solution validation, or early-stage implementations is expected.
- Professional AI/ML experience is a plus but is not mandatory.
- Strong communication, problem-solving, ownership, continuous-learning, and cross-functional collaboration skills.
Benefits
- Hybrid working arrangement.
- Opportunity to join a newly established Private AI and Sovereign AI team at an early stage and influence its technologies and implementation practices.
- Learning and development opportunities in generative AI, enterprise infrastructure, cloud engineering, DevOps, and solution architecture.