Etl Pipeline Developer Jobs

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Today

AI & Data Engineer

EVERFORCE LLC Santa Clara, CA

Santa Clara, CA

Today

$110k - $150k/yr Full time Ai Engineering Ai Ml Api Integration

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Job Description

The AI & Data Engineer at EVERFORCE LLC will design, build, and maintain production-grade data pipelines and ETL/ELT workflows, while developing and operating AI/ML models. This role includes integrating AI and data processing capabilities with enterprise systems and cloud platforms, supported by documentation and governance practices.

Location

Santa Clara, CA (onsite)

Compensation

USD 110,000 - 150,000 per year

Experience

Minimum 2 years

Responsibilities

Design, build, and maintain scalable data pipelines and ETL/ELT processes to ingest, clean, transform, and integrate structured and unstructured data from enterprise systems and other data sources.
Develop, train, test, and deploy machine learning and artificial intelligence models, including, where applicable, integration of large language models (LLMs), prompt engineering, and retrieval-augmented generation (RAG) pipelines.
Integrate AI/ML models and data pipelines with enterprise applications, APIs, and cloud-based platforms.
Monitor, troubleshoot, and optimize performance, quality, and reliability of data pipelines and models, including data validation and issue resolution.
Maintain documentation, metadata, and data lineage to support governance, transparency, and auditability.
Collaborate with IT, data science, and business teams to gather requirements, refine solutions, and support ongoing AI and data initiatives.

Technologies

ETL/ELT
Machine learning
Artificial intelligence
Large language models (LLMs)
Prompt engineering
Retrieval-augmented generation (RAG)
APIs
Cloud-based platforms

Deliverables

Documented, production-ready data pipelines and ETL/ELT jobs, including source-to-target mapping and data quality checks.
Trained, validated, and deployed AI/ML models or model enhancements, along with relevant performance and evaluation metrics.
Integrations that connect AI/ML models and data pipelines to enterprise systems, applications, and/or cloud platforms.
Dashboards, reports, or monitoring tools to track data quality, pipeline health, and/or model performance.
Technical documentation covering data pipeline architecture, data lineage, model design, and deployment processes.
Periodic status updates and knowledge-transfer materials for IT and business teams.

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