Operationalizing AI, mapping requirements to implementation, selecting the appropriate technologies, and evaluating non-functional attributes such as security, usability, and stability.
AI Architect closes this data-to-insight-to-action loop, which requires deep understanding of the applications and integration infrastructure environment. Setting up AI/ML processing environments in IT landscape is a must.
Detailing mapping IT and security process to AI process. Influencing and working with business partners to establish and accelerate AI adoption.
Audit AI solutions for architecture, scalability and security; especially in an enterprise landscape with cloud as apriority.
Mapping requirements to implementation - Analysing, coordinating, prioritizing and optimizing requirements. Ensuring implementation even with constraints.
Selecting technology - selecting appropriate technologies from the many open source, commercial on-premises,and cloud-based offerings available. Integrating a new generation of tools within the existing environment is alsoto help ensure access to accurate and current data. Because technology in this domain is evolving rapidly,
Ensures that components can be replaced with well-suited alternatives that do not require any adjustment or downtime.
Foresight when selecting new technologies like Functional and non functional requirements - considering aspects like security, usability and stability
Leading the ITSAC discussions and working with stakeholders for approval
You will bring !!
Experience in solution architecture of complex cloud-based software/platform and implementation of the same
10+ years of verified hands-on experience building interesting and innovative applications, or equivalent open-source contribution
10+ years of verified experience designing and building software systems. i.e planning out infrastructure, cloudplatform components, etc
Experience in machine learning cloud platforms and components like:
AWS Sagemaker, Azure Databricks or GCP Vertex-AI
MLOps for model training and deployment in production
Model monitoring in production to identify model drift
AWS EKS, Azure AKS and GCP GKE
ETL tools like AWS Glue, Azure Data Factory, GCP Cloud Dataflow
Storage solution like AWS S3, Azure DataLake, GCP BigQuery
Infrastructure as Code (IaC) like CloudFormation, Terraform etc
Experience with one or more programming language such as: Python, Java etc.
Experience in developing and deploying machine learning models on cloud
Hands on experience with Amazon Web Services, Google Cloud Platform, or Azure; namely choosing platform components and solution architecture for machine learning problems on cloud
