AI/ML Engineer

AI in this role

sagemaker

ragfine-tuningml-opsai-safety

Responsibilities

Design, develop, and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, deployment, monitoring, and lifecycle management.
Build scalable, cloud-native AI/ML applications and services using AWS technologies such as SageMaker, ECS, Lambda, S3, EventBridge, and Step Functions.
Develop and maintain machine learning, data engineering, and MLOps pipelines supporting batch and real-time workloads.
Design and implement Generative AI solutions leveraging Large Language Models (LLMs), Advanced RAG, vector databases, knowledge retrieval systems, agentic AI frameworks, and fine-tuning techniques.
Design and utilize knowledge graphs, graph databases, and relationship-based analytics to enhance enterprise intelligence and decision-making.
Partner with business stakeholders to translate business challenges into scalable analytical and AI-driven solutions.
Conduct data discovery and exploratory analysis, establish data lineage, and perform root cause analysis to ensure data quality and reliability.
Implement model monitoring, observability, alerting, and operational support processes for production AI/ML solutions.
Ensure adherence to enterprise AI governance, security, Responsible AI, privacy, and model risk management standards.
Serve as a machine learning engineering subject matter expert, lead technical design discussions, and mentor team members on AI/ML best practices.
Stay current on emerging AI technologies and evaluate their application to business opportunities.

Qualifications

Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; Master's degree preferred.
6+ years of experience in Machine Learning Engineering, Data Engineering, Software Engineering, or a related discipline.
3+ years of hands-on experience building scalable data pipelines and ETL solutions using AWS services.
Strong proficiency in Python and modern software engineering practices.
Experience deploying and supporting production-grade AI/ML applications in cloud environments, preferably AWS.
Strong experience with SageMaker, MLOps, CI/CD pipelines, model deployment, monitoring, and Machine Learning Development Lifecycle (MDLC) practices.
Experience with containerization and orchestration technologies such as Docker, ECS, and Kubernetes.
Experience with Generative AI technologies, including LLMs, Advanced RAG, vector databases, semantic search, agentic AI frameworks, and enterprise knowledge retrieval systems.
Experience designing and implementing knowledge graph solutions and graph databases.
Strong understanding of software engineering fundamentals, including system design, testing, security, observability, and version control.
Ability to lead technical initiatives, influence architectural decisions, and collaborate effectively across business and technology teams.

Preferred Experience

Real-time data processing and streaming technologies such as Kafka, Flink, or Kinesis.
AI governance, Responsible AI, and model risk management frameworks.
Enterprise-scale AI platform development and solution architecture.

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

AI/ML Engineer at Vanguard rates 98 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.

Classification

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

RAGFine TuningML OpsAI SafetySagemaker

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
How do you think about the risk of an AI system in this kind of role failing silently?
Walk me through how you've used Sagemaker in your day-to-day work.

Adapt your resume

List these exact terms on your resume: RAG, Fine Tuning, ML Ops, AI Safety, 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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