AI & Data Engineer

cyberani solutions

The AI & Data Engineer supports the design, development, and operationalization of data pipelines and AI/ML models that power the organization s analytics and intelligent applications. Working under the guidance of senior engineers and data scientists, this role helps collect, clean, store, and transform data, and assists in building scalable machine-learning solutions.

Responsibilities:

1.Data Ingestion & Integration

Assist in building and maintaining ETL/ELT pipelines to ingest data from relational databases, APIs, flat files, and streaming sources.

Use tools such as Apache Airflow, Azure Data Factory, or similar orchestration platforms.

2.Data Storage & Modeling

Support the creation and management of data lakes, data warehouses, and feature stores (e.g., Snowflake, Azure Synapse, BigQuery, Delta Lake).

Help design logical and physical data models that enable efficient querying and analytics.

3.Data Preparation & Quality

Perform data cleaning, validation, and transformation using Python, SQL, or Spark.

Implement basic data quality checks and monitoring alerts.

4.Machine-Learning Model Support

Collaborate with data scientists to prepare training datasets and feature engineering pipelines.

Deploy simple models to production using container platforms (Docker, Kubernetes) or managed services (Azure ML, SageMaker, Vertex AI).

5.Automation & CI/CD

Contribute to version-controlled code repositories (Git) and assist in setting up CI/CD pipelines for data and model deployments.

6. Documentation & Collaboration

Document data lineage, pipeline architecture, and model metadata.

Participate in agile ceremonies (stand-ups, sprint planning) and work closely with cross-functional teams (analytics, product, IT).

7.Learning & Development

Stay current with emerging AI/ML frameworks, cloud data services, and best practices.

Pursue relevant certifications or training (e.g., Azure Data Engineer Associate, Google Cloud Professional Data Engineer).

","requirements":"

• Bachelor s degree in Computer Science, Data Science, Electrical Engineering, Mathematics, or a related field.
• 0 2 years of experience.
• Hands-on experience with data engineering or AI/ML projects.
• Programming: Python (pandas, NumPy) and SQL.
• Data processing: Familiarity with Spark, Pandas, or similar frameworks.
• Cloud platforms: Basic exposure to AWS, Azure, or Google Cloud data services.
• Databases: Experience with relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases.