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.
Databricks
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.

Work arrangement
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