Senior ML Engineer: Production Pipelines (Hybrid)

Vanguard; Malvern (AR); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Vanguard is seeking a senior ML data engineer to design and maintain data pipelines for production model development. You will collaborate with data science teams to ensure robust data ingestion, quality, and governance across the ML lifecycle.

You will leverage AWS services (Glue, SageMaker) and Python-based tooling to optimize ETL workflows, monitor models, and address data quality issues. Hybrid work model with opportunities across teams.

Qualifications

• Undergraduate degree or equivalent experience; a graduate degree is preferred.
• Minimum of 5 years of relevant work experience.
• At least 3 years of hands-on experience designing ETL pipelines using AWS services (Glue, SageMaker).
• Proficiency in Python (including PySpark, PySQL) and familiarity with ML libraries/frameworks.
• Strong understanding of cloud technologies (AWS/Azure) and NoSQL databases.
• Familiarity with Feature Store usage, LLMs, GenAI, RAG, and Model Evaluation.

Responsibilities

• Leverages data pipeline designs and supports the development of data pipelines to support model development.
• Supports integration of model pipelines in a production environment and understands the ML lifecycle.
• Reviews pipeline designs and documents design changes with data science teams.
• Performs data discovery & automated ingestion for model development and analyzes data quality.
• Engages with stakeholders to translate requirements into analytic approaches and supports planning.
• Runs model monitoring scripts and addresses issues from data pipelines.
• Participates in special projects and other duties as assigned.

Skills

Python (PySpark, PySQL)

AWS

Spark/ETL

NoSQL databases

MDLC & ML lifecycle

Education

Bachelors degree or equivalent

Graduate degree preferred

Tools

AWS Glue

SageMaker

Azure

Job description

Vanguard is seeking a senior ML data engineer to design and maintain data pipelines for production model development. You will collaborate with data science teams to ensure robust data ingestion, quality, and governance across the ML lifecycle.

You will leverage AWS services (Glue, SageMaker) and Python-based tooling to optimize ETL workflows, monitor models, and address data quality issues. Hybrid work model with opportunities across teams.