AI Architect (Generative AI)

TL;DR

AI Architect (Generative AI): Designing and deploying scalable AI solutions, including LLM-powered applications and RAG pipelines, with an accent on Generative AI and architectural leadership. Focus on implementing foundation models, optimizing vector database solutions, and establishing enterprise-grade AI governance.

Location: Remote - US

Company

hirify.global provides AI design and implementation services, delivering scalable AI/ML solutions for diverse clients.

What you will do

• Design and architect end-to-end AI/ML and Generative AI solutions aligned with client business objectives.
• Lead development of LLM-powered applications, predictive models, and intelligent automation.
• Architect RAG pipelines and vector database solutions using tools like Pinecone, FAISS, or Weaviate.
• Evaluate and integrate foundation models and establish standards for prompt engineering and AI guardrails.
• Design data pipelines, ETL processes, and oversee MLOps/LLMOps practices.
• Provide technical leadership and architectural guidance while supporting pre-sales and client engagements.

Requirements

• Must be based in the United States.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• 7+ years of experience in software engineering, data engineering, or AI/ML development.
• 3+ years of experience designing and deploying AI/ML architectures at scale.
• Hands-on experience with Generative AI, LLMs, and orchestration frameworks like LangChain or LlamaIndex.
• Proficiency in Python and TypeScript, and experience with cloud platforms (AWS, Azure, or GCP).

Nice to have

• Experience with multimodal AI (text, image, audio, video).
• Knowledge of containerization and orchestration (Docker, Kubernetes).
• Industry experience in healthcare, financial services, or retail sectors.

Culture & Benefits

• Full remote work arrangement within the US.
• Opportunity to lead strategic AI initiatives for high-impact client projects.
• Collaborative environment working with cross-functional teams of data scientists and engineers.