Document architecture, workflows, and best practices for knowledge sharing and compliance.
Provide technical oversight & Guidelines
Architect and implement end-to-end MLOps and LLMOps pipelines using Azure Machine Learning and Azure OpenAI.
Design scalable infrastructure for training, deploying, and monitoring ML and LLM models in production.
Collaborate with data scientists and engineers to streamline model development, testing, and deployment workflows.
Manage Azure Kubernetes Service (AKS) clusters and containerized ML workloads.
Ensure model governance, versioning, and reproducibility using tools like MLflow and Azure DevOps.
Promote DevSecOps practices, ensuring security and compliance are embedded in the ML lifecycle.
Monitor and troubleshoot production ML systems, ensuring high availability and performance.
Qualifications
Experience with Azure Machine Learning, Azure OpenAI, Azure DevOps, and AKS.
Proficiency in Python, Docker, Kubernetes, and CI / CD pipelines.
Experience with LLM fine-tuning, prompt engineering, and model deployment.
Familiarity with MLflow, Terraform, and monitoring tools like Prometheus / Grafana.
J-18808-Ljbffr
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Architect • Jersey City, NJ, US
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