Applied Data Scientist - MLOps

Confidential; United Arab Emirates; On-site; AED 200,000 - 300,000; Full time

Job summary

A leading consulting and IT services firm is seeking an Applied Data Scientist – MLOps to join their UAE operations. The ideal candidate will have a Bachelor’s degree in a relevant field, with strong programming skills in Python and proficiency in ML frameworks like TensorFlow and PyTorch. Key responsibilities include developing data science solutions, deploying ML models, and utilizing MLOps practices. This position requires 3-4 years of relevant experience and offers an opportunity to work with a leading organization in the UAE.

Qualifications

• 3-4 years of hands-on experience as a Data Scientist or ML Engineer
• Proven experience deploying ML, DL, and GenAI models in production environments
• Practical experience working with MLOps workflows

Responsibilities

• Develop data science solutions using ML and modern modeling techniques
• Perform exploratory data analysis (EDA) and feature engineering
• Build, test, and validate supervised and unsupervised ML models
• Implement deployment of ML/AI models into production using CI/CD

Skills

Strong Python programming skills

Proficiency in ML frameworks: TensorFlow, PyTorch, MLflow, Hugging Face

Deep understanding of MLOps tooling

Experience with CI/CD (GitHub Actions, Azure DevOps)

Ability to build APIs (FastAPI, Flask)

Experience with LLMs, RAG pipelines, vector databases

Education

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

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

Tools

Docker

Kubernetes

Azure ML

Job description

VAM Systems is a Business Consulting, IT Solutions and Services company.

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

Qualification

• 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: (15 - 30 days)

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

Skills Required: Data Science - ML, Pandas, Numpy, Tensorflow, MLops, Llm, Api, Azure