Asks for

The ad says“● Hands-on experience designing and deploying Agentic AI applications in production environments.”
The ad says“● Deep expertise in Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and agentic workflows.”
The ad says“● Experience with Azure OpenAI, OpenAI, Anthropic, Gemini, or equivalent enterprise LLM platforms.”
The ad says“● Strong understanding of microservices, APIs, Kubernetes, event-driven architectures, and enterprise integration patterns.”
The ad says“● Strong experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.”
The ad says“● Expertise in cloud-native architectures on Azure, AWS, or Google Cloud.”
The ad says“● Expertise in cloud-native architectures on Azure, AWS, or Google Cloud.”
The ad says“● Experience with Azure OpenAI, OpenAI, Anthropic, Gemini, or equivalent enterprise LLM platforms.”
The ad says“● Strong understanding of microservices, APIs, Kubernetes, event-driven architectures, and enterprise integration patterns.”
The ad says“● Strong understanding of microservices, APIs, Kubernetes, event-driven architectures, and enterprise integration patterns.”
The ad says“● Expertise in Large Language Models (LLMs), foundation models, and Generative AI architectures.”
The ad says“● Experience with Azure OpenAI, OpenAI, Anthropic, Gemini, or equivalent enterprise LLM platforms.”
The ad says“● Expertise in Large Language Models (LLMs), foundation models, and Generative AI architectures.”
The ad says“● Expertise in cloud-native architectures on Azure, AWS, or Google Cloud.”
The ad says“● Strong understanding of microservices, APIs, Kubernetes, event-driven architectures, and enterprise integration patterns.”
The ad says“● Strong experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.”
The ad says“● Strong experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.”
The ad says“● Expertise in Large Language Models (LLMs), foundation models, and Generative AI architectures.”
The ad says“● Strong MLOps and LLMOps experience including model deployment, monitoring, evaluation, and governance.”
The ad says“● Strong understanding of microservices, APIs, Kubernetes, event-driven architectures, and enterprise integration patterns.”
The ad says“● Strong MLOps and LLMOps experience including model deployment, monitoring, evaluation, and governance.”
The ad says“● Experience with Azure OpenAI, OpenAI, Anthropic, Gemini, or equivalent enterprise LLM platforms.”
The ad says“● Deep expertise in Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and agentic workflows.”
The ad says“● Deep expertise in Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and agentic workflows.”
The ad says“● Strong experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.”
The ad says“● Deep expertise in Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and agentic workflows.”

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.