Applied AI Architect

@ Neshent Technologies
Neshent Technologiesneshenttechnologies.com

Applied AI Architect

Northbrook, Illinois
Posted 2 days ago

About the job

We are a company focused on AI/ML solutions. The Applied AI Architect will design, implement, and optimize AI architectures, working closely with teams to deploy advanced AI models and systems in production environments.

Requirements

  • Hands-on experience in AI/ML development
  • Experience with Databricks and Azure AI
  • Knowledge of RAG and LLM architecture
  • Strong Python skills for production
  • Experience with CI/CD and MLOps

Qualifications

  • Bachelor's in Computer Science or related
  • Proven experience in AI/ML architecture
  • Strong problem-solving skills
  • Excellent collaboration abilities

Full job description

We are looking for an Applied AI Architect with strong hands-on experience in AI/ML architecture, development, and production deployment. The ideal candidate will have expertise in Databricks, Azure AI Foundry, LLM applications, RAG, and multi-agent systems.

Must-Have Technical Skills

  • Strong hands-on experience in AI/ML development and production deployment.
  • Experience with Databricks, MLflow, Unity Catalog, Delta Lake, and model serving.
  • Experience with Azure AI Foundry and modern AI/ML platforms.
  • Strong knowledge of RAG and LLM application architecture.
  • Experience building multi-agent systems and workflows.
  • Experience with MCP/tool calling and frameworks such as LangGraph, Semantic Kernel, or OpenAI Agents SDK.
  • Strong Python development skills for production AI/ML applications.
  • Experience with CI/CD, MLOps, and AIOps.
  • Knowledge of LLM/RAG/agent evaluation, observability, tracing, and monitoring.
  • Experience with production debugging and performance optimization.
  • Ability to create reusable AI accelerators, templates, skills, and reference implementations.

Roles & Responsibilities

  • Define and implement AI/ML architecture and solutions.
  • Work closely with engineering and business teams to build and deploy AI models.
  • Design and develop LLM, RAG, and multi-agent solutions.
  • Establish best practices for AI evaluation, deployment, monitoring, and production support.
  • Improve and standardize applied AI delivery patterns.
  • Accelerate AI adoption through reusable components, templates, and reference architectures.
  • Provide technical leadership and guidance to AI/ML engineering teams.
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