Senior ML/LLM Ops Architect – AI Platform Leader

World Wide Technology, Inc.; United States; Remote; USD 125,000 - 187,000; Full time

Benefits offered by this job

Health benefits

401k with company matching

Parental Leave

Paid Time Off

Job summary

World Wide Technology, Inc. seeks a senior, technically minded architect to define how AI, machine learning, and large language model capabilities are designed, deployed, operated, and improved in production.

You will balance hands-on development with architecture leadership, working across engineering, product, data, security, and IT teams to ensure secure, scalable AI solutions and establish robust ML/LLM Ops practices.

Qualifications

• Bachelor’s or Master’s degree in computer science, engineering, information systems, or a related field.
• 12+ years of progressive experience in software engineering, solution architecture, platform engineering, data engineering, or related technical discipline.
• Experience designing, building, or operating production ML/AI systems (generative AI/LLM).
• Strong communication, written, verbal, and presentation skills for technical and non-technical audiences.

Responsibilities

• Lead architecture reviews and communicate trade-offs to technical and non-technical audiences.
• Define ML/LLM Ops standards, runbooks, and operational readiness criteria.
• Collaborate with engineering, product, data, security, and IT teams to ensure secure, scalable AI solutions.

Skills

Software architecture

Excellent communication

Leadership

Cross-team collaboration

Education

Bachelor’s/Master’s in CS/Engineering

Tools

CI/CD

Containerization

IaC

Job description

World Wide Technology, Inc. seeks a senior, technically minded architect to define how AI, machine learning, and large language model capabilities are designed, deployed, operated, and improved in production.

You will balance hands-on development with architecture leadership, working across engineering, product, data, security, and IT teams to ensure secure, scalable AI solutions and establish robust ML/LLM Ops practices.