AI Data Architect

Job Title: AI Data Architect

Experience Required: 8 to 15 Years

Role Overview: We are seeking an inventive

Data Architect for AI with 8–15 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. 1. Databricks 2. 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:

• Bachelor’s or Master’s 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.