Data Science Intern

Job Description & summary

We are seeking a highly skilled Senior Associate specializing in Azure ML Ops to join our team. This is a full-time onsite position where you will play a pivotal role in driving machine learning operations using Azure technologies. With a strong focus on deployment, management, and optimization of ML models, this role is critical in ensuring the seamless integration of AI solutions into business processes.

Responsibilities

• Azure ML Ops Development: Design, implement, and maintain scalable machine learning operations using Azure ML services.
• Model Deployment: Manage end-to-end deployment pipelines for machine learning models to ensure efficient production environments.
• Optimization: Continuously monitor and optimize model performance, resource usage, and operational processes.
• Collaboration: Work closely with data scientists, engineers, and business stakeholders to align ML solutions with organizational goals.
• Documentation: Maintain comprehensive documentation of ML pipelines, workflows, and processes.
• Problem-Solving: Proactively identify and resolve technical issues related to ML Ops.

Work Environment

• Type: Onsite work environment.
• Commitment: Full-time employment with 100% resource allocation to this role.

Why Join Us

• Opportunity to work on cutting-edge machine learning technologies in a dynamic and collaborative environment.
• Be part of a high-impact team that drives innovation and delivers tangible results.
• Competitive salary and benefits package tailored to attract top-tier talent.

Mandatory Skill sets

• Proficiency in Azure ML Ops is mandatory.

Preferred Skill sets:

Experience in industries where machine learning plays a key role in decision-making and automation is highly valued.

Years of Experience Required

• Experience: 6–10 years of relevant experience in machine learning operations, with hands-on expertise in Azure ML Ops.

Education Qualification

• : While no specific educational qualification is required, a background in Computer Science, Data Science, Engineering, or related fields is preferred.