About the role
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 QualificationsExposure to Responsible AI, AI governance, and regulatory compliance frameworks. Relevant cloud, AI, or enterprise architecture certifications.