Senior Agentic AI Scientist for Enterprise LLMs

Talentify; Scottsdale (AZ); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Vanguard is seeking a senior AI practitioner to identify high-impact problems and translate them into agentic AI solutions. You will design systems that reason, plan, and act across tools, driving enterprise value with scalable, responsible AI.

You will collaborate across teams, mentor peers, and help establish best practices for implementing LLM-based workflows in production environments. A strong emphasis on governance and risk-aware deployment is essential.

Qualifications

• Agentic AI: experience designing AI agents that reason, plan, and act across systems.
• LLMs: hands-on experience building enterprise LLM applications (e.g., RAG, tool use, orchestration, evaluation).
• NLP: strong experience working with unstructured text and language-driven workflows.
• MS/PhD in Computer Science, ML, Data Science, or related quantitative field.
• 3+ years delivering AI/ML solutions in production environments.
• 5+ years of hands-on Python experience; distributed data processing is a plus.

Responsibilities

• Identify, frame, and prioritize high-value AI use cases with measurable business impact.
• Design and build agentic AI systems that can reason, plan, and act across tools, workflows, and data sources.
• Ensure solutions are scalable, reliable, and governed for enterprise use.
• Mentor peers and collaborate with stakeholders to drive responsible AI practices.

Skills

Agentic AI

LLMs

NLP

Python

Cross-functional collaboration

Effective communication

Education

MS or PhD in Computer Science / ML / Data Science or related field

Tools

RAG tooling

Orchestration frameworks

Evaluation frameworks

Job description

Vanguard is seeking a senior AI practitioner to identify high-impact problems and translate them into agentic AI solutions. You will design systems that reason, plan, and act across tools, driving enterprise value with scalable, responsible AI.

You will collaborate across teams, mentor peers, and help establish best practices for implementing LLM-based workflows in production environments. A strong emphasis on governance and risk-aware deployment is essential.