Enterprise AI Architect

• Collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases. Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to misaligned initiatives and impractical use cases.
• Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders - business users, data scientists, security professionals, data engineers and analysts, and those in IT operations - and developing processes and products based on the inputs.
• Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings. Select cloud, on-premises, or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools.
• Audit AI tools and practices across data, models, and software engineering with a focus on continuous improvement. Ensure a feedback mechanism to assess AI services, support model recalibration and retrain models.
• Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices.
• AI architecture and pipeline planning. Understand the workflow and pipeline architectures of ML and deep learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment and production workflows of AI is a must.
• Software engineering and DevOps principles, including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD.
• Data science and advanced analytics, including knowledge of advanced analytics tools (such as SAS, R and Python) along with applied mathematics, ML and Deep Learning frameworks (such as TensorFlow) and ML techniques (such as random forest and neural networks).
• Thought leadership. Be change agents to help the organization adopt an AI-driven mindset. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of IT executives who provide overall digital thought leadership.
• Collaborative mindset. To ensure that AI platforms deliver both business and technical requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders and CxOs (technical and nontechnical personnel), and harmonize the relationships among them.

Must have: Data Science GenAI Tech Stack LLMOps MLOps Python

JD: Experience in leading and delivering enterprise AI platform architectural thinking, and its practical application.

• Experience in the use of conceptual and logical data modelling technologies.
• Experience in defining and working with information and data regulatory governances.
• Experience in designing modular, scalable AI architectures for NLP and agent-based systems.
• Experience in AI engineering such as MLOps, LLMOps, containerization, orchestration, etc.
• Experience in known industry IT architectural patterns and IT architecture ways of working/methodologies (e.g. Amazon Bedrock, Amazon Quick Suite and Sagemaker).
• Understands AI Platforms concepts and cloud-based containerization strategies for hybrid cloud environments.
• Experience in the appropriate AI structure and technology based on business use case and completely familiar with AI lifecycles.
• Hands on experience coding in Python, IaC, etc.