W2- Cupertino, CA / New York City, NY / Austin, TX (Hybrid) :: Data Engineer with MLOps, LLM and GenAI/RAG Exp. (Only G.C / U.S.C)

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Data Engineer with MLOps, LLM and GenAI/RAG Exp. (Only G.C / U.S.C)

6+Months

Cupertino, CA / New York City, NY / Austin, TX (Hybrid, Onsite 3 days a week)

Core Competence: Data Engineering with a track record of 6 to 9 years

Role Insight: The incumbent will spearhead the development of advanced data infrastructures that drive the functionality of Wallet, Payments, and Commerce initiatives. The engineer will concentrate on crafting and optimizing high-caliber data pipelines to support analytics and machine learning objectives, ensuring robust data structure design and system scalability.

PRINCIPAL ACCOUNTABILITIES:

Data Engineering & Systematic Blueprint:

Craft and scale both batch and real-time proximate data processing systems.

Perfect ETL/ELT mechanisms to ensure optimized execution and economic feasibility.

Deploy structured data representation and consistently define business analytics.

Enhance application interfaces and consumer interactions through comprehensive data collection.

Data Stewardship & Integrity:

Uphold stringent data accuracy, regime, privacy, and regulation adherence.

Guarantee the steadfastness and accessibility of vital organizational platforms.

PROFESSIONAL PREREQUISITES:

Over half a decade of expertise in data engineering, targeting analytical or machine learning endeavors.

Exceptional command over SQL.

Proficiency in programming with Python, Scala, or Java.

Practical skill set with big data tools like Spark, Kafka, and workflow orchestrators like Airflow or equivalents.

Deep familiarity with contemporary data construction and Lakehouse architecture concepts (e.g., Iceberg).

Experience managing cloud environments such as AWS, Azure, or Google Cloud Platform.

Willingness to support systems on a rotating call basis.

Applied knowledge of data warehousing solutions like Snowflake, Databricks, fast analytics on Trino, OLAP/NRT databases, visualization with Superset or Tableau.

Grounding in Continuous Integration/Continuous Deployment (CI/CD), data monitoring, and declarative infrastructure setup.

Proficiency in MLOps practices and involvement with Generative AI/RAG infrastructure.

Practical engagement with Large Language Models, including prompting strategies, fine-tuning practices, and RAG.

Background in Financial Technology, Wallet solutions, or Payment systems.