Applied AI Architect
@ Neshent TechnologiesApplied AI Architect
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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