Job Description: – 

• 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 skills 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· Emotion and Sentiment Analytics· 

• Custom Speech model – Speech-to-text and Voice synthesis calibrated for language, accent, pitch, tone, noise and business vocabs. · 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.· 

•  Enterprise cloud Data repository –Cloud Storage, NoSQL DB, Graph DB, Data Lake· Data Ingestion, Transformation, Analytics – Real-time and batches·  

• Enterprise Security for AI/ML – Active Directory – Authentication, Authorization· Web app and services – Micro services, Azure functions, Logic apps, API management- Ability to compare and contrast across technologies in any layer objectively with evaluation criteria and considerations 

•  Excellent communication skills, preparing PowerPoint presentations, executive readouts