8–10 years of relevant professional experience across AI/ML, data, cloud, infrastructure or enterprise technology, with strong hands-on architecture and engineering capability.
Strong hands-on architecture and engineering capability, including Python.
Experience with Kubernetes, Docker, Terraform and modern cloud/infrastructure environments.
Familiarity with modern AI ecosystems such as PyTorch, Hugging Face, LangChain, LlamaIndex and vector databases.
Understanding of RAG, MLOps/LLMOps, AI evaluation and agentic AI architectures.
Exposure to AI governance, security, bias/risk mitigation, red-teaming or responsible AI.
Strong grounding in enterprise data, data quality and integration.
Proven ability to lead or mentor technical teams and engage confidently with enterprise customers.
Experience in regulated industries, private AI or sovereign environments is a strong advantage.