Profile Overview:

We are searching for a MLOps engineer to join our growing data science team! In this role, you will develop, deploy, and maintain models in the client’s IT environment. You will collaborate with cross-functional teams (Data Scientists, Cloud Engineers, IT, etc.).

 Responsibilities:

• Work closely with US stakeholders (Analytics, Business, IT) to understand modeling requirements and translate them into effective deployment solutions.
• Collect, clean, and pre-process data from various sources to ensure accuracy and completeness.
• Deploying and maintaining machine learning models on Azure, including setting up Azure Machine Learning workspaces, managing Azure Databricks clusters, and using Azure DevOps for CI/CD pipelines.
• Develop technical schematics to support deployments.
• Develop data pipelines and automation processes with Azure Data Factory.
• Design, develop, and implement machine learning algorithms (e.g., linear regression, decision trees, random forests, and deep learning) to solve specific business challenges. This involves selecting appropriate algorithms, tuning hyperparameters, and training and evaluating models to achieve optimal performance.
• Co-ordinate with IT to ensure timely and effective deployment and maintenance of ML models.
• Perform rigorous statistical modeling and analysis to uncover relationships and trends within the data.
• Develop and deploy robust APIs using FastAPI to integrate machine learning models into applications.
• Monitor and manage the performance and scalability of deployed models and applications on Azure.
• Document the findings and recommendations clearly and concisely. 

Skills:

• Programming: Python (Pandas, Scikit-Learn, FastAPI)
• Data Analysis: Statistical Understanding, Hypothesis Testing, and EDA Proficiency.
• Machine Learning: Familiarity with algorithms like linear regression, decision trees, and random forests.
• Version Control: Manage projects with Git (or similar)
• Data Wrangling: Master of Data Cleaning, Manipulation, and Transformation (Pandas)
• Azure: Deploying machine learning applications
• Communication: excellent written and verbal communication for seamless collaboration with US Clients.

Qualifications:

• 2-3 years of experience in developing, deploying, and maintaining ML Models in production environments (MLOps Engineer or similar role)
• Experience in software development, particularly with Python and FastAPI preferred.
• Hands-on experience with Azure for machine learning tasks, including deploying and managing applications and solutions.
• Ability to think critically and solve problems creatively.
• Strong attention to detail and a commitment to data quality.
• Knowledge of insurance and financial services a plus.
• Communication experience with US clients and PowerPoint a plus.