VanguardMachine Learning Engineer, Specialist
AI in this role
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.
How we rate this
Machine Learning Engineer, Specialist at Vanguard rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide that one model's output is better than another's for a given task?
- What's a project where you used Sagemaker hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering, RAG, AI Evaluation, and Sagemaker. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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