Description :

- Design, plan, and implement AI, Generative AI, and Agentic AI architectures for customer projects.

- Develop custom end-to-end GenAI and LLM based solutions, using frameworks such as LangChain, LangGraph, AutoGen, HuggingFace, and OpenAI APIs.

- Implement and operate machine learning systems using TensorFlow, PyTorch, and modern LLM/ML tooling.

- Build and manage cloud-based AI ecosystems on AWS, Azure, or GCP, including architecture design and platform operations.

- Apply DevOps/MLOps and LLMOps practices, including CI/CD pipelines, containerization (Docker, Kubernetes), and model deployment workflows.

- Collaborate closely with enterprise architects, engineering teams, and C-level stakeholders to align AI roadmaps and solution strategies.

- Evaluate and introduce new AI technologies, frameworks, and agentic tools to expand the organizations (Gen)AI capabilities.

- Lead the technical execution of proof-of-concepts (PoCs) in an agile environment.

- Provide strategic consulting on cloud-native, scalable, and secure AI platform architectures.

Requirements :

- Degree in (Business) Computer Science, Engineering, or a comparable technical field.

- 5+ years of experience as a Solutions Architect or Enterprise Architect in cloud/data environments.

- Proven experience developing and integrating LLM or Generative AI solutions into production systems.

- Hands-on project experience designing complex cloud architectures on AWS, Azure, or GCP.

- Strong knowledge of AI/ML development with Python and major ML frameworks (TensorFlow, PyTorch, HuggingFace).

- Practical experience with agentic AI frameworks (e.g., LangGraph, AutoGen) and LLM orchestration tools.

- Experience with MLOps, DevOps, and container technologies such as Docker and Kubernetes; familiarity with CI/CD for ML models.

- Understanding of architecture frameworks and agile methods (Scrum, Kanban).

- Excellent English communication skills.

- Willingness to travel, depending on project requirements.