Machine Learning Engineer, Specialist

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

Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment.

Core Responsibilities

• Leverages data pipeline designs and supports the development of data pipelines to support model development. Proficient with software tools that develop data pipelines in a distributed computing environment (PySprak, GlueETL).

• Supports integration of model pipelines in a production environment. Develops understanding of SDLC for model production.

• Reviews pipeline designs, makes data model design changes as needed. Documents and reviews design changes with data science teams.

• Supports data discovery & automated ingestion for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs.

• Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.

• Runs model monitoring scripts, follows process for alerts to management as needed. Addresses issues found in data pipelines from model monitoring alerts.

• Participates in special projects and performs other duties as assigned.

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 (e.g., Glue, SageMaker).

• Proficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks.

• Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.

• Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.

• Experience with API design and development is a plus.

• Solid understanding of software engineering principles, including design patterns, testing, security, and version control.

• Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.

• Understanding of solution architecture for building end-to-end machine learning data pipelines.

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don’t just have a mission—we’re on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients’ lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.