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# Principal AI Engineer (Technical Lead)

- **Company:** Experis
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $187,200.0 - $197,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Fastapi, AI Platforms
- **Published:** September 10, 2026
- **Apply:** https://www.experis.com/en/job/410481/principal-ai-architect-ibm-watsonx-agentic-ai

## About the Role

Our client, a leading organization in the technology and enterprise solutions sector, is seeking a Principal AI Engineer (Technical Lead) to join their team. As a Principal AI Engineer (Technical Lead), you will be part of the Experis Practice supporting client engagements. The ideal candidate will demonstrate strong leadership, technical expertise, and innovative problem-solving skills, aligning successfully within the organization., + 8-10+ years of professional software engineering experience, with at least 3 years in a technical lead role on multi-person teams

 + Hands-on experience with enterprise AI platforms (e.g., Azure OpenAI, Google Vertex AI, AWS Bedrock, or comparable solutions within large-scale partner ecosystems)
 + Proficiency in Python 3.10+ with experience deploying production services using FastAPI or similar frameworks
 + Experience designing and deploying RAG pipelines with vector databases such as Milvus, OpenSearch, or pgvector
 + Strong client-facing communication skills, with the ability to present technical solutions to non-technical stakeholders

## Description

+ Serve as the technical authority and trusted delivery lead for AI-driven engagements within the practice
 + Design, develop, and deliver conversational AI solutions using modern orchestration and assistant platforms, implementing enterprise patterns for client use cases
 + Lead multi-agent AI system architecture, including defining agent capabilities, action connectors, and workflows, leveraging frameworks such as LangGraph for complex orchestration
 + Build and optimize RAG (Retrieval-Augmented Generation) pipelines utilizing vector databases such as Milvus and OpenSearch, including document ingestion, embedding, and retrieval workflows
 + Mentor and lead technical teams, conduct code reviews, and ensure adherence to architectural standards and best practice


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