Senior AI/ML Scientist

AI in this role

ragai-agentsnlpai-safety

Responsibilities:

Solve Business Problems with AI

Partner with business stakeholders to identify, frame, and prioritize high‑value problems that can be addressed using Agentic AI, LLMs, and NLP.
Translate business needs into clear AI use cases, success metrics, and delivery plans.
Focus on business outcomes, not just model performance.

Design & Build Agentic AI Solutions

Architect and develop agentic AI systems that can reason, plan, and take actions across tools, workflows, and data sources.
Design multi‑agent and tool‑augmented LLM solutions to automate complex, multi‑step processes.
Ensure solutions are reliable, explainable, and governed for enterprise use.

Scalable & Responsible AI

Collaborate with engineering teams to deploy AI solutions with scalability, security, and performance in mind.
Implement evaluation, monitoring, and guardrails for LLM and agentic systems, including bias, drift, and failure modes.
Align solutions with enterprise risk management, compliance, and responsible AI standards.

Thought Leadership & Collaboration

Act as a trusted AI advisor, helping teams understand where Agentic AI and LLMs add value—and where they do not.
Contribute to AI best practices, reusable patterns, and strategic direction.
Mentor peers and teammates on applied AI and business‑driven problem solving.

Qualifications:

Agentic AI: Experience designing AI agents that reason, plan, and act across systems.
Large Language Models (LLMs): Hands‑on experience building enterprise LLM applications (e.g., RAG, tool use, orchestration, evaluation).
Natural Language Processing (NLP): Strong experience working with unstructured text and language‑driven workflows.

MS or PhD in Computer Science, Machine Learning, Data Science, or a related quantitative field.
3+ years delivering AI/ML solutions in production environments.
5+ years of hands‑on Python experience; experience with distributed data processing is a plus.
Strong ability to solve business problems using AI, not just build models.
Excellent communication skills, with the ability to explain complex concepts to both technical and non‑technical audiences.
Experience working in cross‑functional, enterprise environments.

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

Senior AI/ML Scientist at Vanguard rates 91 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

RAGAI AgentsNLPAI Safety

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
What NLP problem have you worked on, and how did you measure whether it actually worked?
How do you think about the risk of an AI system in this kind of role failing silently?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: RAG, AI Agents, NLP, and AI Safety. 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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