Artificial Intelligence Architect

The Artificial Intelligence Architect will design and implement enterprise-scale AI and Data Science solutions on cloud platforms. This role involves defining AI architecture, selecting technologies, and auditing AI tools. Key responsibilities include understanding ML/Deep Learning pipelines, hands-on programming in C#.net, Node.js, or Python, and expertise in cloud AI/ML PaaS components. The architect will also work with NLP, custom speech models, computer vision, entity extraction, and cognitive search, ensuring robust deployment and integration.

Must Have

• Design and implement AI and Data Science solutions on Cloud at enterprise scale.
• Define AI architecture and select appropriate technologies.
• Audit AI tools and practices across data, models and software engineering.
• Understand the workflow and pipeline architectures of ML and Deep Learning workloads.
• Hands-on programming skill on at least one language – C#.net or node.js or python.
• Expert on Cloud competencies on "Artificial Intelligence" and "Machine Learning" PaaS components.
• Experience with Contextual Conversation design (Azure BOT service/Amazon Lex/ Google Dialog flow).
• Experience with Natural Language Processing model design, training and publishing for multiple languages.
• Experience with Custom Speech model (Speech-to-text and Voice synthesis).
• Proficiency in Computer vision (OCR, Face Recognition, Custom model for Object detection and classifications).
• Ability to perform Document/Form Entity Extraction and Cognitive Search on heterogeneous unstructured data.
• Experience with Enterprise Channel Integration for AI.
• Experience with Deployment and publish for AI and ML services with ACR, ACI, Docker, Azure Kubernetes.
• Certifications on AI & Data Science (Azure/ AWS/ GCP/ Watson like AI 102, DP 100).

Job Description:

• Design and Implement Artificial Intelligence and Data Science solutions on Cloud at enterprise scale.
• Contextual understanding of customer requirements with visions, strategies, and roadmaps for implementation.
• Collaboration with solution architects, business analysts and partner architects/stakeholders in Global Delivery model for AI Solution development.
• Play a key role in defining 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
• Understand the workflow and pipeline architectures of ML and Deep Learning workloads.
• 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).
• Hands-on programming skill on at least one language – C#.net or node.js or python.
• – Expert on Cloud competencies on “Artificial Intelligence” and “Machine Learning” PaaS components.
• Contextual Conversation design (Azure BOT service/Amazon Lex/ Google Dialog flow) – for personalized and humanized interaction with end user for complex business cases.
• Natural Language Processing model – design, training and publishing for multiple languages.
• Custom Speech model – Speech-to-text and Voice synthesis calibrated for language, accent, pitch, tone, noise and business vocab.
• Computer vision – OCR, Face Recognition, Custom model for Object detection and classifications.
• Document/Form Entity Extraction.
• Cognitive Search on heterogeneous unstructured data.
• Enterprise Channel Integration for AI.
• Deployment and publish for AI and ML services with ACR, ACI, Docker, Azure Kubernetes.
• Azure/ AWS/ GCP/ Watson certifications on AI & Data Science like AI 102, DP 100.