KSA
ML Ops Engineer
Uplift People Consulting
- Industry Type
- Air Conditioning, Refrigeration, HVAC
- Functional Area
- Artificial Intelligence Machine Learning AIML Industry AirConditioning, Refrigeration, HVAC
- Nationality
- Any
- Keywords
- and Alerting Collaboration with Backend and Platform Teams
Job Description
Uplift People Consulting in Riyadh, Saudi Arabia, is looking for an ML Ops Engineer to deploy and operate machine learning models in production. The role involves managing model serving, monitoring, logging, and alerting while working closely with backend and platform teams.
- Position
- ML Ops Engineer
- Company
- Uplift People Consulting
- Location
- Riyadh, Saudi Arabia
- Industry
- Air Conditioning, Refrigeration, HVAC
- Experience
- 1 to 20 years
Responsibilities
- Deploy and operate machine learning models in production environments.
- Manage model serving, monitoring, logging, and alerting.
- Collaborate with backend and platform teams.
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About the role: ML Ops Engineer
General information about this type of role, not from the employer. The employer's actual requirements may differ, so always check the job details above.
ML Ops Engineers focus on deploying and maintaining machine learning models in production environments. They work closely with data scientists and IT teams to ensure that AI solutions operate efficiently and reliably. These professionals are commonly employed in industries like air conditioning, refrigeration, and HVAC to improve system performance and predictive maintenance.
What this role usually involves
- Develop and deploy machine learning models into production systems.
- Monitor and maintain the performance of ML models over time.
- Collaborate with data scientists to understand model requirements and improvements.
- Automate workflows related to data processing and model deployment.
- Ensure scalability and reliability of ML infrastructure.
- Troubleshoot and resolve issues related to ML systems.
Skills employers usually look for
- Knowledge of machine learning frameworks and tools.
- Experience with cloud platforms and containerization technologies.
- Strong programming skills, especially in Python or similar languages.
- Understanding of data pipelines and ETL processes.
- Ability to work with cross-functional teams.
- Problem-solving and analytical thinking.
Usual qualifications
- A degree in computer science, engineering, or a related field is usually expected.
- Familiarity with machine learning and data engineering concepts.
- Certifications in cloud computing or ML operations can be beneficial.
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