Role Summary Value Creed is seeking an experienced AI Architect to lead the design and implementation of enterprise-grade AI solutions, with a strong focus on Agentic AI, Generative AI, and AI-native applications within the Energy Trading & Risk Management (ETRM) domain. The ideal candidate will be responsible for architecting AI-ready platforms, establishing robust AI governance and guardrails, and partnering with clients to design scalable, secure, and production-ready AI solutions that drive business value. Key Responsibilities ● Partner with clients and business stakeholders to identify AI use cases and translate requirements into scalable technical solutions. ● Lead architecture discussions, solution design workshops, and technical governance reviews. ● Define and implement enterprise AI architecture and technology roadmaps aligned with business objectives. ● Design and deploy AI-ready infrastructure with appropriate governance, security, compliance, observability, and responsible AI guardrails. ● Establish reference architectures, best practices, and reusable frameworks for GenAI and Agentic AI implementations. ● Architect and implement scalable Agentic AI solutions using modern orchestration frameworks. ● Design multi-agent workflows, autonomous reasoning systems, tool integrations, and human-in-the-loop capabilities. ● Develop Retrieval-Augmented Generation (RAG) architectures leveraging enterprise knowledge sources. ● Design and implement MLOps and LLMOps frameworks for model lifecycle management, deployment, monitoring, governance, and optimization. ● Build scalable data pipelines and knowledge ingestion frameworks to support AI workloads. ● Enable AI adoption across ETRM processes including trading operations, risk management, analytics, and decision support. Required Skills & Qualifications ● Expertise in Large Language Models (LLMs), foundation models, and Generative AI architectures. ● Hands-on experience designing and deploying Agentic AI applications in production environments. ● Deep expertise in Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and agentic workflows. ● Strong experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks. ● Experience with Azure OpenAI, OpenAI, Anthropic, Gemini, or equivalent enterprise LLM platforms. ● Strong MLOps and LLMOps experience including model deployment, monitoring, evaluation, and governance. ● Expertise in cloud-native architectures on Azure, AWS, or Google Cloud. ● Strong understanding of microservices, APIs, Kubernetes, event-driven architectures, and enterprise integration patterns. ● Experience building large-scale data ingestion and processing pipelines. Preferred Qualifications Exposure to Responsible AI, AI governance, and regulatory compliance frameworks. Relevant cloud, AI, or enterprise architecture certifications.