Data Scientist & MLOps

VAM Systems; Dubai; On-site; AED 200,000 - 300,000; Full time

Job summary

A prominent IT solutions provider in Dubai is seeking a Data Scientist – MLOps to drive data science solutions and model deployment. The role requires strong Python skills, experience in deploying ML models, and a solid understanding of MLOps workflows. Candidates should possess a Bachelor's degree in a relevant field and have 3-4 years of hands-on experience. This position allows you to work closely with a leading organization in the UAE while contributing to cutting-edge AI applications.

Qualifications

• 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.

Responsibilities

• Develop design and develop data science solutions using traditional ML and modern modeling techniques.
• Implement lead deployment of ML/AI models into production using CI/CD, automation, and containerized workflows.
• Deploy LLM-powered applications, including prompt-based models, fine-tuned models, and RAG systems.

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

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

Education

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field

Master’s degree or certifications in ML/AI/MLOps

Job description

Overview

VAM Systems is currently looking for Data Scientist – MLOps for our UAE operations with the following skillsets & terms and conditions:

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Master’s 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.

Responsibilities

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: maximum 4 weeks

The selected candidates shall join VAM Systems - UAE and shall be deputed to one of the leading organizations in UAE.