AI Data Engineer
•Today
| Verified Pay check_circle | Provided by the employer$100000 to $115000 per year |
|---|---|
| Hours | Full-time, Part-time |
| Location | Tampa, FL Tampa, Florida open_in_new |
About this job
Job Description
Job Description
Job Description
Job Role: AI Data Engineer
Job Location: Tampa, FL (Day 1 Onsite)
Job Type: Full Time
Salary Range: $100000 to $115000/Annum Full Time Benefits
Must Have Technical/Functional Skills
Job Role: AI Data Engineer
Job Location: Tampa, FL (Day 1 Onsite)
Job Type: Full Time
Salary Range: $100000 to $115000/Annum Full Time Benefits
Must Have Technical/Functional Skills
- Strong hands-on experience in Python, SQL and data engineering.
- Experience designing and developing ETL/ELT pipelines for large-scale data processing.
- Strong knowledge of PySpark / Spark for data transformation and processing.
- Experience with AWS data services such as Glue, S3, Redshift and related cloud services.
- Hands-on experience with Apache Airflow or equivalent orchestration tools.
- Strong understanding of data transformation, cleansing, validation, reconciliation and data-quality frameworks.
- Experience working with structured, semi-structured and unstructured data.
- Knowledge of batch and streaming data pipelines, preferably Kafka.
- Experience building data pipelines supporting AI/ML, GenAI, LLM and RAG use cases.
- Experience with CI/CD, Git, Docker and Kubernetes/EKS for production deployments.
- Good understanding of data modeling, performance Eligible to workimization and production support.
- Design, develop and maintain scalable end-to-end ETL/ELT and data pipelines.
- Build data processing and transformation solutions using Python, SQL and PySpark.
- Develop batch and streaming pipelines to integrate data from databases, APIs, files and enterprise source systems.
- Implement data-quality checks, validation, reconciliation, deduplication and exception handling.
- Eligible to workimize data pipelines, SQL queries and Spark workloads for high-volume processing.
- Prepare and transform datasets required for AI/ML and Generative AI applications.
- Develop data ingestion and preparation pipelines supporting LLM/RAG solutions and vector databases.
- Collaborate with AI/ML engineers, application teams and business stakeholders on downstream data requirements.
- Implement automated deployment and monitoring through CI/CD and DevOps practices.
- Support production deployments, monitoring, troubleshooting and root-cause analysis for data-related issues.
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