Solution Architect (AI)
Summary
Solution Architect designing AI/ML solutions for enterprise clients, with hands-on engineering in Python, cloud infrastructure (Kubernetes, Docker, Terraform), and modern AI ecosystems (LangChain, Hugging Face, vector databases).
• 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.