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AI Observability Engineer
Job in
Charlotte, Mecklenburg County, North Carolina, 28202, USA
Listed on 2026-07-27
Listing for:
Merican
Full Time
position Listed on 2026-07-27
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, AI Reliability/ Performance Engineer
Job Description & How to Apply Below
AI Observability Engineer
We are looking for an experienced AI Observability Engineer to design, deploy, and monitor enterprise AI/ML solutions with a strong focus on Generative AI, LLMs, RAG, and AI observability. The ideal candidate will have hands-on experience building scalable AI applications, implementing monitoring frameworks, and optimizing AI model performance in production environments.
Key Responsibilities:
- Design and develop enterprise-grade Generative AI applications using OpenAI, AWS Bedrock, and Hugging Face.
- Build and optimize Retrieval-Augmented Generation (RAG) solutions using Lang Chain, Lang Graph, vector embeddings, and Azure AI Search.
- Develop Agentic AI workflows and multi-agent orchestration solutions.
- Optimize LLM inference using LoRA, QLoRA, vLLM, Paged Attention, and continuous batching.
- Build REST APIs and AI microservices using Python and FastAPI.
- Develop and maintain ML pipelines for model training, deployment, monitoring, and lifecycle management.
- Implement AI observability using tools like Arize to monitor model performance, prompt quality, hallucinations, latency, and inference metrics.
- Establish AI governance, evaluation frameworks, and guardrails for responsible AI.
- Develop machine learning models using PyTorch, Scikit-learn, and XGBoost.
- Build analytics dashboards and provide AI-driven insights to stakeholders.
- Deploy containerized applications using Docker, Git Hub, and CI/CD pipelines.
- Collaborate with cross-functional teams to deliver scalable AI solutions.
Required Skills:
- Strong experience with Generative AI, LLMs, and RAG architectures.
- Hands-on experience with OpenAI, AWS Bedrock, Hugging Face, Lang Chain, and Lang Graph.
- Proficiency in Python and FastAPI.
- Experience with AI observability platforms such as Arize.
- Knowledge of PyTorch, Scikit-learn, and XGBoost.
- Experience with Docker, Git Hub, and CI/CD pipelines.
- Strong understanding of AI governance, model evaluation, and responsible AI practices.
- Excellent analytical, problem-solving, and communication skills.
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