AI Data Engineer

Job Title: AI Data Engineer
Exp- 4 to 8 years
Location- PAN India
Job Description
Key Responsibilities:
• Build and maintain data infrastructure: Design and construct scalable, reliable data pipelines, storage, and processing systems in the cloud.
• Ensure data quality: Clean, transform, and enrich raw data to create business truth that AI models can use for accurate insights.
• Enable AI/ML: Make data readily available and optimized for consumption by AI and machine learning models.
• Manage cloud services: Work with cloud-specific services for storage, compute, and networking to build an efficient and scalable AI data environment.
• Implement security and governance: Apply security controls to protect data and ensure compliance within the data platforms.
• Monitor and optimize: Continuously monitor data workloads and optimize for performance and cost-effectiveness.
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Essential skills and tools
• Cloud Platforms: Deep knowledge of data services at least one major cloud provider (e.g., AWS, Google Cloud).
• Programming Languages: Strong proficiency in Python, Spark and SQL.
• Data Warehousing & Storage: Experience with technologies like Azure Synapse Snowflake, GCP BigQuery, Databricks, AWS Redshift and Data Lake.
• Data Pipelines: Familiarity with tools like Azure Data factory, AWS Glue, Apache Airflow, Kafka and dbt for orchestrating data workflows.
• AI-specific tools: Knowledge of vector databases
• Infrastructure as Code (IaC): Skills in tools like Bicep, Terraform or CloudFormation to automate infrastructure deployment.
• CI/CD: Understanding of continuous integration and continuous deployment pipelines.

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Experience with any of the following Cloud Native Data Services:
• Azure: Azure Data Factory, MS Fabric, Azure Databricks, Azure Synapse Analytics, Datalake Gen2 and Azure Dedicated SQL Pool (ADW), Cosmos DB
• AWS: AWS Glue, AWS S3, AWS Athena, AWS Kinesis and AWS Redshift, Dynamo DB
• Google Cloud Platform (GCP): GCP Dataproc, GCP DataFlow, GCP BigQuery, GCP Cloud Storage, Cloud SQL and Pub Sub, Google BigTable, Google Spanner.
Qualifications:
• Bachelor’s or master’s degree in engineering or technology
• Proven experience in building and deploying ETL/ELT solutions in production.
• Strong understanding of Data models and Data pipelines and cloud-native Big data architectures.
• Excellent problem-solving, communication, and collaboration skills.