AI Architect

As an AI Architect, you will be expected to:

• Design and build advanced Agentic AI and GenAI solutions, taking ownership of the technical execution from rapid PoC to reliable production deployment using agile methodologies and best practices.
• Apply state-of-the-art analytical techniques to architect solutions from complex structured and unstructured data ecosystems.
• Evaluate and integrate the latest Agentic AI platforms, multi-agent frameworks, and foundational models to ensure the best strategic fit for the Deloitte landscape.
• Architect end-to-end data pipelines, models, and AI applications, leveraging cloud platforms (Azure AI/ML Studio, AWS Bedrock, GCP Vertex) and incorporating standard MLOps/LLMOps/AgentOps frameworks.
• Build, optimize, and deploy production-grade fine-tuned LLMs, complex RAG architectures, and integrated autonomous workflows.
• Design, architect, and manage advanced prompts and utilize Context Engineering to optimize language model reasoning and Agent outputs.
• Communicate effectively with cross-functional stakeholders using data visualisation, storytelling, and clear presentation of complex AI concepts.
• Implement and champion frameworks for the ethical use of AI, ensuring strict adherence to data privacy regulations, AI governance policies, and enterprise security standards.

Connect to your skills and professional experience

• Minimum of 7 years of experience in data science, machine learning, and AI, with a proven track record of delivering AI-driven solutions in a professional setting, using a variety of tools and techniques.
• Highly proficient in Python, with deep hands-on expertise in AI libraries and modern development frameworks (e.g., LangGraph, CrewAI, PyTorch, etc.).
• Expert in implementing Agentic AI solutions, MCP protocols and integrating with GenAI based applications.
• Expertise in designing and implementing Agentic AI solutions, Model Context Protocol (MCP), A2A and integrating complex autonomous workflows into existing enterprise applications.
• Strong experience architecting and deploying solutions on major cloud platforms (Azure, AWS, and GCP), with a solid grasp of LLMOps/AgentOps and scalable cloud-native architectures.
• Solid understanding of GenAI observability and monitoring frameworks (e.g., LangSmith, Langfuse) to ensure system performance and trace reasoning chains.
• Advanced expertise in prompt engineering, evaluation-based agent development, and context optimization.
• Prior experience implementing ethical AI practices, data privacy safeguards, and AI governance.
• Expertise in prompt engineering.