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Applied AI Engineer

Description

We are seeking a highly skilled Applied AI Engineer to join our innovative team. In this role, you will be instrumental in bridging the gap between theoretical AI research and practical, high-impact business applications. You will design, develop, and deploy a range of machine learning models, from sophisticated recommendation systems and predictive analytics tools to fine-tuned generative AI solutions for enterprise challenges. Working with diverse datasets including text, images, and structured data, you will build robust, end-to-end AI pipelines. You will collaborate closely with product managers and data scientists to define problem spaces and deliver scalable, efficient, and cost-effective AI systems. This position requires a hands-on engineer passionate about leveraging the latest advancements in AI to solve real-world problems and drive measurable outcomes.

Requirements

1. 3-5 years of professional experience in an AI, machine learning, or data science role with a focus on deployment.

2. Strong proficiency in Python and associated ML libraries (e.g., scikit-learn, Pandas, NumPy).

3. Hands-on experience with at least one major deep learning framework, such as TensorFlow or PyTorch.

4. Demonstrable experience deploying and managing ML models on a major cloud platform (AWS SageMaker, Google AI Platform, Azure ML).

5. Proven experience with LLM fine-tuning techniques (e.g., LoRA, full fine-tuning) and the Transformer architecture.

6. Practical knowledge of building systems with vector databases (e.g., Pinecone, Milvus, Weaviate) and Retrieval-Augmented Generation (RAG).

7. Experience designing and implementing end-to-end MLOps pipelines for model training, validation, and serving.

8. Solid understanding of software engineering best practices, including version control, testing, and CI/CD.

Desirable

1. Experience with containerization (Docker) and orchestration (Kubernetes) for deploying scalable services.

2. Familiarity with data processing at scale using tools like Apache Spark or Dask.

3. Contributions to open-source AI/ML projects or a portfolio of relevant personal projects.

4. Experience with model optimization techniques such as quantization, pruning, or knowledge distillation.

5. Strong communication skills with experience presenting complex technical concepts to non-technical stakeholders.

Role Highlights

πŸ’° Compensation

AED 45,000 – AED 55,000

πŸ“ Location

Dubai, UAE

πŸ’Ό Work Location Type

Hybrid

πŸ“ˆ Job Level

Mid Level

βŒ› Experience

3–5 years of experience in machine learning, data science, or AI engineering.

🏒 Department

Information Technology

🏭 Industry

Technology, Information & Media

πŸ”Ή Sub-Industry

AI Development

Total Applications :

0


Important information

How did you hear about us? *

Which country's passport do you hold? *

Email *(Please ensure the email matches the one mentioned in your CV or resume)

LinkedIn Profile URL *

Please provide your current and expected salary in the box below: *

Please mention your notice period *

Please answer the following 6 short questions. These help our team better understand your strengths and areas of experience.

It’s completely fine to select β€˜No’ if something doesn’t apply to your background β€” we’re looking for a good fit, not a perfect one.

If you select β€˜Yes’ for any question, you’re welcome to use it as a guide to highlight relevant experience in your CV, where applicable.

1. Do you have at least 3 years of professional experience deploying machine learning models into production environments? *

2. Have you fine-tuned a large language model and integrated it into an application using a RAG architecture? *

3. Do you have hands-on experience using a managed AI platform like AWS SageMaker, Google AI Platform, or Azure Machine Learning for model deployment? *

4. Have you built an automated, end-to-end machine learning pipeline from data ingestion to model inference? *

5. Does your experience include building models using both structured (e.g., tabular) and unstructured (e.g., text, image) data? *

6. Have you been directly responsible for optimizing a production machine learning model for cost, latency, or throughput? *

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