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Job Title: ML Ops Specialist - Senior

 

Experience: 2-4 years

 

 

Job Summary: 

 

We are seeking a skilled ML Ops Specialist to join our team in the banking and insurance sectors. The ideal candidate will have a solid background in ML Ops and AI Ops, with hands-on experience in deploying and managing machine learning models in production environments, particularly using Azure. This role will focus on ensuring the reliability, scalability, and efficiency of AI solutions within our organization.

 

 

Key Responsibilities:

 

Design, implement, and manage ML Ops pipelines to streamline the deployment and monitoring of machine learning models in production. Collaborate with data scientists and engineers to ensure smooth integration of ML models into existing systems and workflows. Utilize Azure services for deploying, managing, and scaling machine learning applications and infrastructure. Monitor model performance and implement strategies for continuous improvement and optimization. Develop and maintain documentation for ML Ops processes, workflows, and best practices. Implement AI Ops practices to enhance operational efficiency and reliability of AI systems. Troubleshoot and resolve issues related to model deployment, performance, and data quality. Stay updated on industry trends and advancements in ML Ops and AI Ops, recommending best practices for implementation.

 

 

Qualifications:

 

Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. 2-4 years of experience in ML Ops and AI Ops, preferably in the banking and insurance sectors. Proficiency in programming languages such as Python or R, with experience in ML frameworks (e.g., TensorFlow, PyTorch). Strong understanding of Azure services related to machine learning (e.g., Azure Machine Learning, Azure DevOps). Experience with CI/CD practices for machine learning model deployment. Excellent analytical and problem-solving skills, with a focus on delivering reliable AI solutions. Strong communication skills and the ability to work effectively in a collaborative environment.

 

 

Preferred Skills:

 

Familiarity with containerization technologies (e.g., Docker, Kubernetes) for deploying ML models. Knowledge of data governance and compliance standards in the banking and insurance sectors. Experience with monitoring and logging tools for AI systems.

 

 

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