# ML Engineer

## Summary
- Organization: Career Soft Solutions
- Location: Dubai
- Type: Full-Time
- Department: Engineering
- Status: active
- Posted: [object Object]
- Updated: [object Object]
- Closing Date: 2026-11-26
- External Apply: No
- External Apply URL: support@talentreview.ai

## Details
- Salary: N/A
- Experience: 5-9 years
- Education: B.tech
- Team: N/A
- Reporting To: N/A

## Description
Job Description: Machine Learning Engineer (MLOps)

Job Title: Machine Learning Engineer (MLOps)
Location: Dubai, UAE
Work Arrangement: On-site – Candidates must currently be based in Dubai
Duration: 12 Months
Monthly Rate: AED 11,000
Experience: 5–9 Years
Employment Type: Contract

**Position Summary**

We are seeking an experienced Machine Learning Engineer (MLOps) to develop, deploy, monitor, and optimise machine-learning models and AI pipelines for production environments. The ideal candidate will have strong expertise in Python, machine-learning frameworks, MLOps practices, cloud platforms, and containerisation technologies.

The candidate will be responsible for transitioning machine-learning models from proof of concept (PoC) to scalable production deployment across cloud and edge environments, ensuring performance, reliability, and resource efficiency.

Important: Only candidates currently residing in Dubai, UAE, will be considered.

**Key Responsibilities**

Model Deployment: Deploy machine-learning models and AI pipelines from development and PoC stages into production using CI/CD pipelines, containerisation, and orchestration tools.

Monitoring and Maintenance: Implement model monitoring, performance tracking, drift detection, and maintenance strategies to ensure accuracy and reliability.

Model Optimisation: Optimise models for inference speed and resource efficiency using quantisation, pruning, and knowledge distillation techniques.

Data Preprocessing: Perform data collection, cleaning, transformation, and feature engineering to prepare datasets for training and evaluation.

Model Training and Tuning: Develop continuous or semi-continuous training and evaluation workflows, fine-tune models, and maintain model performance over time.

MLOps Implementation: Build and maintain automated ML pipelines, model versioning, experiment tracking, and deployment workflows.

Cloud and Edge Deployment: Deploy and optimise ML solutions across cloud platforms and edge devices, including resource-constrained environments.

Cross-Functional Collaboration: Work closely with data scientists, software engineers, DevOps teams, and product managers to translate business requirements into effective ML solutions.

Documentation: Maintain comprehensive documentation of source code, models, deployment processes, pipelines, and operational procedures.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, or a related field.

5–9 years of relevant experience in machine learning engineering, AI engineering, or MLOps.

Strong programming skills in Python.

Hands-on experience with PyTorch, TensorFlow, NumPy, Pandas, and Scikit-learn.

Experience deploying machine-learning models in production environments.

Knowledge of Docker, Kubernetes, and CI/CD pipeline implementation.

Experience working with SQL and NoSQL databases.

Familiarity with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).

Experience with MLOps tools such as MLflow, ClearML, Azure Machine Learning, AWS SageMaker, or Kubeflow.

Strong understanding of deep learning, reinforcement learning, model evaluation, and optimisation techniques.

Understanding of model monitoring, performance tuning, and production troubleshooting.

Preferred Qualifications

Experience deploying computer-vision models to edge devices or low-resource environments.

Familiarity with infrastructure-as-code tools and observability platforms.

Experience with GPU-based computing and model acceleration.

Contributions to open-source computer-vision projects or relevant research publications.

Experience with distributed computing frameworks and large-scale ML pipelines.

Core Technical Skills

Programming Language

Python

Frameworks and Libraries

PyTorch, TensorFlow, OpenCV, Scikit-learn, Pandas, NumPy, FastAPI

MLOps and Model Management

MLflow, Kubeflow, ClearML, Ray

Deployment and CI/CD

Docker, Kubernetes, GitLab CI/CD

Databases and Search Technologies

PostgreSQL, MySQL, MongoDB, Elasticsearch, Neo4j

Deep Learning Architectures

CNN, LSTM, GANs, Transformers, Large Language Models (LLMs)

Distributed Computing, Messaging and GPU Technologies

RabbitMQ, Kafka, CUDA, RAPIDS, Numba

Cloud Platforms

AWS, Microsoft Azure, Google Cloud Platform (GCP)

Candidate Requirements

Must currently be based in Dubai, UAE.

Must be available to work on-site in Dubai.

Must be comfortable with a 12-month contract.

Must be able to work within the proposed monthly rate of AED 11,000.

Application Process

Interested candidates who meet the above requirements are encouraged to share their updated CV, current location, total experience, relevant MLOps experience, notice period, and expected monthly rate for consideration.

## Responsibilities
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## Skills
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## Tags
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## Organization
- Name: Career Soft Solutions
- Website: http://www.cssinc.ai
- Industry: Staff Augmentation
- Size: 400+
- Founded Year: 2015
- Description: N/A