Data Scientist – MLOps

VAM Systems is currently looking forData Scientist MLOpsfor 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 -UAEand shall be deputed to one of the leading organizations inUAE .

Additional Information :

Terms and conditions:

Joining time frame: maximum 4 weeks

Remote Work :

No

Employment Type :

Full-time