Solution Architect (AI)

• 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.