AI/ML Model Deployment Engineer

Experis; Basking Ridge (NJ); On-site; USD 76,000 - 90,000; Full time

Benefits offered by this job

Medical plans

Vision plan

HSA

Health FSA

Dependent Care FSA

Life Insurance

Short/Long Disability

Travel Insurance

Weekly Pay

Job summary

Experis is seeking a Data Scientist for onsite work in Basking Ridge, NJ to join the Data Analytics Department and support AI/ML model deployment and lifecycle management.

The ideal candidate will have 5-7 years of data science experience, strong ML algorithm knowledge, and a proven ability to develop scalable data models and pipelines, analyze complex problems, and manage model testing and lifecycle processes.

Qualifications

• 5-7 years of experience in data science, AI/ML deployment, or related fields.
• Strong understanding of machine learning algorithms and data mining techniques.
• Experience building scalable, high-performance data models and pipelines.
• Ability to analyze complex problems and translate insights into system requirements.
• Proficiency in testing, validation, and lifecycle management of AI models.

Responsibilities

• Support the deployment of various AI/ML models into production environments.
• Troubleshoot activities and resolve issues within the end-to-end model pipeline.
• Test and validate AI models, ensuring quality and performance throughout their lifecycle.
• Industrialize the model pipeline, including testing and validation processes.
• Contribute to requirements for new enhancements in models and solutions, proposing innovative ideas to improve project outcomes.

Skills

AI/ML deployment

Data pipelines

Model testing

Lifecycle management

Problem analysis

Job description

Experis is seeking a Data Scientist for onsite work in Basking Ridge, NJ to join the Data Analytics Department and support AI/ML model deployment and lifecycle management.

The ideal candidate will have 5-7 years of data science experience, strong ML algorithm knowledge, and a proven ability to develop scalable data models and pipelines, analyze complex problems, and manage model testing and lifecycle processes.