AI Platform Engineer: MLOps, LLMs & Semantic AI

Programmers.io; Dallas (TX); On-site; USD 180,000 - 250,000; Full time

Job summary

Programmers.io seeks an experienced AI Platform Engineer to operationalize AI-DLC practices across product, architecture, and engineering teams. You will drive AI-assisted development, prompt and context engineering, and establish standards for semantic knowledge assets.

Responsibilities include coaching teams on validation frameworks, human-in-the-loop controls, and integrating MLOps/LLMOps pipelines. You will support legacy modeling efforts and build reusable best practices for scalable

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 8+ years of software engineering, architecture, QA, DevOps, or platform engineering experience.
• Hands-on experience delivering cloud-native and enterprise applications.
• Experience with AI-assisted development tools and modern software engineering practices.
• Strong coding and solution architecture background.

Responsibilities

• Work hands-on with Product, Architecture, Engineering, QA, and UX teams to operationalize AI-DLC practices.
• Enable Mob Construction, AI-assisted development, prompt engineering, and context-driven delivery.
• Establish reference architectures, AI coding standards, reusable prompts, and semantic knowledge assets.
• Coach teams on validation frameworks, AI quality practices, and human-in-the-loop controls.
• Support brownfield semantic modeling and legacy system knowledge extraction.
• Introduce AI engineering toolchains, evaluation pipelines, and MLOps/LLMOps practices.
• Capture delivery learnings and convert them into reusable best practices.

Skills

Software engineering

Architecture

Cloud-native

QA & DevOps

MLOps

Education

Bachelor's degree in CS/Engineering

Tools

AWS

Azure

GCP

LLMOps tooling

Job description

Programmers.io seeks an experienced AI Platform Engineer to operationalize AI-DLC practices across product, architecture, and engineering teams. You will drive AI-assisted development, prompt and context engineering, and establish standards for semantic knowledge assets.

Responsibilities include coaching teams on validation frameworks, human-in-the-loop controls, and integrating MLOps/LLMOps pipelines. You will support legacy modeling efforts and build reusable best practices for scalable