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.