MLOps Engineer (Istanbul / Ankara / Izmir) at Huawei Technologies Co. Ltd - Singapore

İzmir, Aegean Region, Turkey -

Full Time

Start Date

Immediate

Expiry Date

13 Jan, 26

Salary

0.0

Posted On

15 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Bash, Docker, Kubernetes, Linux, Git, CI/CD, MLflow, Kubeflow, Airflow, Cloud Platforms, Model Monitoring, Logging Frameworks, Nvidia GPUs, Huawei Ascend NPUs, API Development, Microservice Architectures

Industry

Telecommunications

Description

About the Role Huawei Turkey R&D Center is seeking a skilled MLOps Engineer to support our AI projects by building and maintaining scalable, reliable, and efficient MLOps infrastructures. You will be responsible for managing the end-to-end lifecycle of machine learning models — from experimentation to deployment and monitoring — while working closely with data scientists and software engineers. Key Responsibilities Automate ML model training, testing, deployment, and monitoring pipelines Design and maintain CI/CD workflows for ML systems Manage model versioning, experiment tracking, and model serving processes Collaborate with data science teams to optimize model performance in production Implement cloud-based MLOps solutions (Huawei Cloud or similar platforms) Ensure model observability, traceability, and security across all environments · Establish and maintain comprehensive monitoring and logging systems (e.g., Prometheus, Grafana, ELK stack) to ensure system health and proactive issue resolution. · Configure and optimize the performance of Nvidia GPUs for training and inference workloads. Experience with Huawei Ascend NPUs is a strong advantage and will be a key focus on the job. Bachelor’s or Master’s degree in Computer Science, Electrical & Electronics Engineering, or related fields Minimum 2 years of experience in MLOps, DevOps, or ML infrastructure engineering Strong experience with Python, Bash, Docker, Kubernetes, Linux and Git Hands-on experience with CI/CD tools (Jenkins, GitLab CI, ArgoCD, etc.) Experience with ML pipeline tools such as MLflow, Kubeflow, or Airflow Familiarity with cloud platforms (Huawei Cloud, AWS, GCP, or Azure) Understanding of model monitoring and logging frameworks (Prometheus, Grafana, ELK Stack Preferred Qualifications Experience optimizing or deploying ML models in production Experience with API development or microservice architectures Familiarity with LLM, NLP or Computer Vision model deployment

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Responsibilities

The MLOps Engineer will manage the end-to-end lifecycle of machine learning models, including automation of training, testing, deployment, and monitoring pipelines. They will collaborate with data scientists to optimize model performance and implement cloud-based MLOps solutions.