VAM Systems is currently looking for Data Scientist MLOps for our UAE operations with the following skillsets & terms and conditions:
Qualification:
- Bachelors degree in Computer Science Data Science Engineering or a related field.
- Masters degree or certifications in ML/AI/MLOps are an advantage.
Experience:
- 3-4 years of hands-on experience as a Data Scientist or ML Engineer with strong focus on model deployment.
- Proven experience deploying ML DL and GenAI models in production environments.
- Practical experience working with MLOps workflows including model training versioning deployment monitoring and automation.
Skills:
- Strong Python programming skills (Pandas NumPy Scikit-learn).
- Proficiency in ML frameworks: TensorFlow PyTorch MLflow Hugging Face.
- Deep understanding of MLOps tooling: MLflow Airflow Kubeflow Docker Kubernetes Azure ML.
- Experience with CI/CD (GitHub Actions Azure DevOps).
- Ability to build APIs (FastAPI Flask) and containerized deployments.
- Experience with LLMs RAG pipelines vector databases (FAISS Pinecone) and prompt engineering.
Responsibities
Data Science & Analytics:
- Develop Design and develop data science solutions using traditional ML and modern modeling techniques.
- Perform exploratory data analysis (EDA) feature engineering and data preprocessing for model development.
- Define measurable success metrics including accuracy precision recall throughput and latency.
Machine Learning Model Development:
- Contribute Build test and validate supervised and unsupervised ML models using best practice methodologies.
- Evaluate multiple algorithms and optimize hyperparameters to improve model robustness.
- Maintain documentation and ensure model interpretability where applicable.
MLOps- End to End Model Deployment:
- Implement Lead deployment of ML/AI models into production using CI/CD automation and containerized workflows.
- Develop reproducible ML pipelines for training testing serving and monitoring.
- Implement scalable APIs and microservices for model inference.
- Set up real time and batch inference systems ensuring reliability and uptime.
- Detect and respond to model drift data drift and performance degradation.
Generative AI / LLMs Deployment
- Deploy LLM-powered applications including prompt based models fine tuned models and RAG systems.
- Build scalable back end infrastructure for hosting LLMs using Azure OpenAI Hugging Face or equivalent platforms.
- Evaluate LLM outputs for accuracy safety and consistency enforcing enterprise guidelines.
Microsoft Automation & Engineering
- Develop automation scripts (Python/CLI) to optimize data pipelines monitoring alerts and deployment workflows.
- Work with APIs microservices and event driven architectures to support ML deployments.
Terms and conditions
Joining time frame: (15 - 30 days)
The selected candidates shall join VAM Systems - UAE and shall be deputed to one of the leading organizations in UAE .
Additional Information :
Terms and conditions:
Joining time frame: maximum 4 weeks
Remote Work :
No
Employment Type :
Full-time
VAM Systems is currently looking for Data Scientist MLOps for our UAE operations with the following skillsets & terms and conditions: Qualification:Bachelors degree in Computer Science Data Science Engineering or a related field.Masters degree or certifications in ML/AI/MLOps are an advantage.Expe...
VAM Systems is currently looking for Data Scientist MLOps for our UAE operations with the following skillsets & terms and conditions:
Qualification:
- Bachelors degree in Computer Science Data Science Engineering or a related field.
- Masters degree or certifications in ML/AI/MLOps are an advantage.
Experience:
- 3-4 years of hands-on experience as a Data Scientist or ML Engineer with strong focus on model deployment.
- Proven experience deploying ML DL and GenAI models in production environments.
- Practical experience working with MLOps workflows including model training versioning deployment monitoring and automation.
Skills:
- Strong Python programming skills (Pandas NumPy Scikit-learn).
- Proficiency in ML frameworks: TensorFlow PyTorch MLflow Hugging Face.
- Deep understanding of MLOps tooling: MLflow Airflow Kubeflow Docker Kubernetes Azure ML.
- Experience with CI/CD (GitHub Actions Azure DevOps).
- Ability to build APIs (FastAPI Flask) and containerized deployments.
- Experience with LLMs RAG pipelines vector databases (FAISS Pinecone) and prompt engineering.
Responsibities
Data Science & Analytics:
- Develop Design and develop data science solutions using traditional ML and modern modeling techniques.
- Perform exploratory data analysis (EDA) feature engineering and data preprocessing for model development.
- Define measurable success metrics including accuracy precision recall throughput and latency.
Machine Learning Model Development:
- Contribute Build test and validate supervised and unsupervised ML models using best practice methodologies.
- Evaluate multiple algorithms and optimize hyperparameters to improve model robustness.
- Maintain documentation and ensure model interpretability where applicable.
MLOps- End to End Model Deployment:
- Implement Lead deployment of ML/AI models into production using CI/CD automation and containerized workflows.
- Develop reproducible ML pipelines for training testing serving and monitoring.
- Implement scalable APIs and microservices for model inference.
- Set up real time and batch inference systems ensuring reliability and uptime.
- Detect and respond to model drift data drift and performance degradation.
Generative AI / LLMs Deployment
- Deploy LLM-powered applications including prompt based models fine tuned models and RAG systems.
- Build scalable back end infrastructure for hosting LLMs using Azure OpenAI Hugging Face or equivalent platforms.
- Evaluate LLM outputs for accuracy safety and consistency enforcing enterprise guidelines.
Microsoft Automation & Engineering
- Develop automation scripts (Python/CLI) to optimize data pipelines monitoring alerts and deployment workflows.
- Work with APIs microservices and event driven architectures to support ML deployments.
Terms and conditions
Joining time frame: (15 - 30 days)
The selected candidates shall join VAM Systems - UAE and shall be deputed to one of the leading organizations in UAE .
Additional Information :
Terms and conditions:
Joining time frame: maximum 4 weeks
Remote Work :
No
Employment Type :
Full-time
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