Applied AI Engineer

# Applied AI Engineer Zendy Sharjah , AEPosted 5/31/2026### AI Match Score Based on your profile & preferences 0%
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** Applied AI Engineer
** role in
** Sharjah, AE**? This position at
** Zendy
* * is an excellent opportunity for professionals looking to advance their career. Browse the full job description below to see if you're a match and add it to your board.###

Job Description Responsibilities:

- Build and maintain robust production ML systems ensuring reliability, scalability, and real-time data processing.

- Utilize AWS Sage Maker for model deployment and monitoring, leveraging AWS services for scalable ML solutions.

- Apply software engineering principles to develop and maintain ML systems with clean, maintainable, and efficient code.

- Develop and deploy models using ML frameworks such as Huggingface, PyTorch, Tensor Flow, and scikit-learn.

- Collaborate with data scientists, software engineers, and stakeholders to implement and optimize ML models.

Requirements:

- Bachelor’s degree in Computer Science, Software Engineering, Data Science, or related field; master’s preferred.

- 3-5 years professional experience in applied AI engineering or related field.

- Strong background in production ML systems.

- Proficiency in Python and ML frameworks:
Huggingface, PyTorch, Tensor Flow, scikit-learn.

- Solid understanding of AWS Sage Maker and related AWS ML services.

- Strong software engineering skills including coding, testing, version control.

- Experience optimizing latency and throughput of ML systems and GPU workloads.

- Excellent problem-solving and communication skills.

Desired Extras:

- Knowledge of retrieval-augmented generation (RAG) techniques.

- Rust programming knowledge.### Ace the Zendy Interview Our AI has generated the most probable interview questions for this role based on the job description and industry standards.

Technical Execution & Core Skills Describe a scenario where you deployed a machine learning model using AWS Sage Maker. How did you ensure scalability and reliability in the production environment?

Problem-solving & Adaptability Can you share an experience where you optimized the latency and throughput of a machine learning system? What challenges did you face and how did you overcome them?

Communication & Teamwork Explain how you have collaborated with data scientists and software engineers to implement and optimize ML models in a production setting.### Interested?

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