Task and Responsibilities:

Solution Architect for Generative AI is responsible for designing, architecting, and development of AI product/service. The main responsibilities include:

• Designing, developing, and deploying Azure-based AI solutions, including machine learning models, cognitive services, and data analytics solutions.
• Collaborating with cross-functional teams, such as data scientists, business analysts, and developers, to design and implement AI solutions that meet business requirements.
• Building and training machine learning models using Azure Machine Learning, and tuning models for optimal performance.
• Developing and deploying custom AI models using Azure Cognitive Services, such as speech recognition, language understanding, and computer vision.
• Creating data pipelines to collect, process, and prepare data for analysis and modeling using Azure data services, such as Azure Data Factory and Azure Databricks.
• Implementing data analytics solutions using Azure Synapse Analytics or other Azure data services.
• Deploying and managing Azure services and resources using Azure DevOps or other deployment tools.
• Monitoring and troubleshooting deployed solutions to ensure optimal performance and reliability.
• Ensuring compliance with security and regulatory requirements related to AI solutions.
• Staying up-to-date with the latest Azure AI technologies and industry developments, and sharing knowledge and best practices with the team.

Qualifications:

• Overall 8+ years’ combined experience in IT and recent 5 years as AI engineer.
• Bachelor's or master's degree in computer science, information technology, or a related field.
• Experience in designing, developing, and delivering successful AI Services.
• Experience with cloud computing technologies, such Azure.
• Relevant industry certifications, such as Microsoft Certified: Azure AI Engineer, Azure Solution Architect etc. is a plus.
• Excellent written and verbal communication skills to collaborate with cross-functional teams and communicate technical information to non-technical stakeholders.

Technical skills

• Proficiency in programming languages: You should be proficient in programming language such as Python and R.
• Experience of Azure AI services: You should have experience with Azure AI services such as Azure Machine Learning, Azure Cognitive Services, and Azure Databricks.
• Data handling and processing: You should be proficient in data handling and processing techniques such as data cleaning, data normalization, and feature extraction. Knowledge of SQL, NoSQL, and big data technologies such as Hadoop and Spark are also beneficial.
• Experience with Cloud platform: You should have a good understanding of cloud computing concepts and experience working with Azure services such as containers, Kubernetes, Web Apps, Azure Front Door, CDN, Web Application firewalls etc.
• DevOps and CI/CD: You should be familiar with DevOps practices and CI/CD pipelines. This includes tools such as Azure DevOps & Git.
• Security and compliance: You should be aware of security and compliance considerations when building and deploying AI models in the cloud. This includes knowledge of Azure security services, compliance frameworks such as HIPAA and GDPR, and best practices for securing data and applications in the cloud.
• Machine learning algorithms and frameworks: Knowledge of machine learning algorithms and frameworks such as TensorFlow, Keras, PyTorch, and Scikit-learn is a plus.