Engenious is recruiting an experienced AI/ML Engineer for a project with our client. The project focuses on developing, deploying, and managing AI/ML models within a Microsoft Azure-based infrastructure. We are seeking a specialist skilled in MLOps practices, responsible for end-to-end model workflows, including data preprocessing, model deployment, monitoring, and CI/CD automation.

Responsibilities

• Develop, deploy, and manage AI/ML models using frameworks like TensorFlow, PyTorch, and Scikit-learn.
• Set up and maintain workflows on Azure Machine Learning (AML) for model training, deployment, and lifecycle management.
• Prepare, process, and engineer features on large datasets, leveraging platforms such as Databricks.
• Design and implement CI/CD pipelines for AI/ML models using Azure DevOps, applying MLOps best practices.
• Configure and manage containerized environments with Docker, Kubernetes, and Azure Kubernetes Service (AKS) for model deployment and scaling.
• Automate model testing and build scripts using Python.
• Monitor and log model performance metrics with Azure Monitor and Application Insights to ensure stability and optimize model performance.
• Collaborate effectively with cross-functional teams to communicate model insights and performance metrics.
• (Bonus) Work on applications involving Retrieval-Augmented Generation (RAG).

Desired Skills and Experience:

• Proficiency in AI/ML frameworks – experience with TensorFlow, PyTorch, and Scikit-learn for model development.
• Hands-on experience with Azure Machine Learning (AML) – ability to manage ML model training, deployment, and performance tracking on AML.
• Data engineering skills – expertise in data preparation, transformation, and feature engineering, ideally using Databricks.
• CI/CD expertise – experience designing and maintaining CI/CD pipelines for AI/ML models using Azure DevOps.
• Familiarity with containerization and orchestration tools – practical knowledge of Docker, Kubernetes, and AKS.
• Strong Python skills – proficiency in automation and scripting for model deployment and testing.
• Experience in monitoring and logging – skilled in using Azure Monitor, Application Insights, and logging metrics for model optimization.
• Statistical modeling skills – capability to apply statistical methods and communicate insights effectively with teams.
• Nice to have: experience with Retrieval-Augmented Generation (RAG) applications.

Location: Remote

Project Duration: 6-9 months (with potential extension)