Remote Senior AI Architect for Production Systems

AHS Staffing; Edmond (OK); Remote; USD 150,000 - 180,000; Full time

Job summary

Trio WFS is seeking a Lead AI Engineer to own architecture and technical direction for the Digital Worker platform. You’ll shape agentic workflows, RAG architectures, and scalable AI infrastructure while collaborating with Product, Data, Platform, and executive stakeholders.

This hands-on leadership role guides a small team, focusing on design reviews, evaluation, and measurable improvements in quality, latency, and cost as production AI capabilities scale.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Information Systems, or related field, or equivalent practical experience.
• Master's degree preferred in Computer Science, AI, ML or related technical discipline.

Responsibilities

• Own the architecture of the Digital Worker platform and AI capabilities.
• Establish design principles, engineering patterns, and architectural standards for AI systems.
• Lead technical design reviews and tradeoff calls on quality, latency, cost, and complexity.
• Evaluate emerging AI technologies and determine adoption worthiness.
• Design and build production systems using LLMs, retrieval architectures, and agentic workflows.
• Build reusable frameworks for agent orchestration, tool integration, and memory.
• Solve the hardest technical problems hands-on.
• Own standards for AI quality: accuracy, groundedness, safety, latency, business impact.

Skills

Software architecture

Mentoring engineers

LLM systems

Agentic workflows

Evaluation frameworks

Education

Bachelor's degree

Master's degree (preferred)

Tools

Azure cloud

Azure OpenAI

Event-driven infra

Vector databases

Semantic search

Embeddings

CosmosDB

Auth0

Azure DevOps

Datadog

Job description

Trio WFS is seeking a Lead AI Engineer to own architecture and technical direction for the Digital Worker platform. You’ll shape agentic workflows, RAG architectures, and scalable AI infrastructure while collaborating with Product, Data, Platform, and executive stakeholders.

This hands-on leadership role guides a small team, focusing on design reviews, evaluation, and measurable improvements in quality, latency, and cost as production AI capabilities scale.