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AI Observability Engineer

Job in Charlotte, Mecklenburg County, North Carolina, 28202, USA
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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