AI Data Architect

Job description

**Role Overview:** We are seeking an inventive **Data Architect for AI** with 8 to 15 years of experience to lead the strategic design and implementation of enterprise-scale AI solutions. This role requires deep expertise in designs, develops, and deploys scalable and secure **data architectures** on cloud platforms to **support AI** **& ML** initiatives. They bridge the gap between business needs and technical implementation by creating the necessary infrastructure for **data processing, model training**, and **inference**. This role requires expertise in cloud services, distributed computing, that handle **large** **datasets** and **complex** **workloads** for AI/ML frameworks, and MLOps to build robust systems **Key Responsibilities:** - **Architectural and Design:** Create and document scalable, secure, and cost-effective data architecture in the cloud (AWS/Azure/GCP) to support AI/ML data workloads. - **Solution development:** Build, optimize, and deploy end-to-end data solutions, such as recommendation data processing engines and data analytic engines. - **Data Engineering:** Proficiency in Data pipelines, ETL processes, Big Data Analytics and data management (SQL, NoSQL, data cleaning). - **Technical implementation:** Select and implement appropriate technologies, including data lakes, batch processing, real-time processing systems and MLOps tools. - **Collaboration**: Work with stakeholders, data scientists, and other teams to translate business requirements into technical specifications and ensure successful technical delivery. - **System management:** Ensure the reliability, performance, and security of Data & AI intensive systems. **Skills:** - **Cloud Platforms**: Deep knowledge and expertise in cloud data services and **ANY ONE** cloud platforms (AWS or Azure OR Google Cloud). - **Data and analytics**: Experience in **ANY ONE** of the following data platforms, Data modeling, and Distributed computing frameworks. A. Databricks 1. Snowflake - **AI/ML knowledge**: Experience in machine learning frameworks, platforms, and MLOps (Machine Learning Operations) practices. - **Programming and scripting**: Proficiency in languages like Python, Spark and SQL for data manipulation and system development. - **Technical communication**: Strong ability to document architectures and communicate complex technical concepts to both technical and non-technical audiences. **Experience with ANY ONE of the following** **Cloud Native Data Services**: - **Azure**: Azure Data Factory, MS Fabric, Azure Databricks, Azure Synapse Analytics, Datalake Gen2, Stream Analytics and Azure Dedicated SQL Pool (ADW), - **AWS**: AWS Glue, AWS S3, AWS Athena, AWS Kinesis and AWS Redshift / EMR - **Google Cloud Platform (GCP)**: GCP Dataproc, GCP DataFlow, GCP BigQuery, GCP Cloud Storage, Cloud SQL and Pub Sub. - Other public cloud platforms such as Snowflake, Hadoop **Qualifications:** - Bachelors or Masters degree in Engineering or Technology. - Proven track record of delivering enterprise Data solutions on a scale. - Strong understanding of Data models and Data pipelines and cloud-native data architectures. - Excellent communication, stakeholder management, and leadership skills.