Design and deliver enterprise AI solutions using agentic AI, LLMs, NLP, RAG, multi-agent systems, and tool orchestration. Partner with stakeholders to identify high-value use cases, define success metrics, and focus on business outcomes. Build scalable, reliable, explainable, and governed systems with evaluation, monitoring, and guardrails. Advise teams on responsible AI adoption, contribute to strategic best practices, and mentor colleagues.
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.
Vanguard Charlotte, North Carolina, USA Office
Two North Falls Plaza, Charlotte, NC, United States, 28217
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