Applied Data Scientist

Applied Data Scientist – MLOps

[01/27/26]

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.
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 .

Should you be interested in this opportunity, please send your latest resume at the earliest at nishanthini.suda@vinirma.com

Job Type:
Permanent

Location:

Salary:
[n/a]

Date available:
[n/a]

Company:
VAM Systems

Company Description:
[n/a]

Company Website:
www.vamsystems.com

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