Kıdemli MLOPS Mühendisi

Alt Bilgi
Bu ilan aracılığıyla yapacağınız başvurular kapsamında toplanacak kişisel verileriniz veri sorumlusu sıfatıyla Sahibinden Bilgi Teknolojileri Pazarlama ve Ticaret Anonim Şirketi (“Sahibinden”) tarafından 6698 sayılı Kişisel Verilerin Korunması Kanunu (“KVKK”) ve ilgili mevzuat uyarınca iş başvuru süreçlerinin yürütülmesi ve iş başvurunuzun değerlendirilmesi amaçlarıyla işlenecektir.”

“İşe alımlarımızda fırsat eşitliğine önem veririz.  Çeşitlilik, Eşitlik ve Kapsayıcılık Politikamız doğrultusunda, her başvuru sürecini şeffaf ve adil bir şekilde değerlendirerek din, dil, ırk, cinsiyet ayrımı yapmaksızın her adayın yeteneklerine odaklanırız.”

Welcome sahibinden.com world!

• We work for WOW? moments
• We stay curious and explore, we love innovation
• We look into the same topics from different perspectives
• We celebrate and feel proud of our success together

S- technology;

Sahibinden.com has been helping people meet their dreams for the last 24 years.

With more than 55 million users and approximately with its 1000 employees, sahibinden.com is among the top five companies globally in classifieds sector.

Approved in 2017 as an R&D center in Turkey, sahibinden.com has been the pioneer of a variety of new services and provide continuous and reliable service over the web and mobile networks. Through our dynamic and agile teams, we continue to be a driving force in the digital marketplace by developing scalable and high-performance platforms that serve millions of users daily.

At sahibinden.com, we adopt a culture of innovation and continuous improvement at work to empower our employees. This approach, which fosters creative thinking and professional development, consistently earns us the title of one of The Best Workplaces in Europe, as voted by our employees.

Join us and be a part of our journey to redefine the future of e-commerce.

Is that you? Great!

• At least 2 years of hands-on experience in MLOps and machine learning model lifecycle management.
• Strong expertise in MLFlow, including tracking experiments, model versioning, and deployment workflows.
• Experience with CI/CD pipelines for automating model deployment and updates.
• Hands-on experience in managing machine learning models in production Kubernetes clusters  environments.
• Strong proficiency in programming languages like Python and familarity with ML libraries.
• Solid understanding of containerization tools such as Docker and container orchestration with Kubernetes.
• Experience with version control and collaboration tools (e.g., Git, GitLab).
• Strong communication skills with an ability to collaborate effectively across teams.

If the requirements are met, then your job description is below;

We are looking for a skilled and motivated MLOps Engineer to join our growing team. The ideal candidate will have extensive experience in MLFlow, MLOps architecture, and Kubernetes clusters. This role focuses on building, deploying, and managing end-to-end machine learning pipelines, ensuring the smooth transition of models from development to production, and implementing scalable solutions for model training, versioning, and deployment. Design, implement, and manage MLOps pipelines for training, versioning, testing, and deploying machine learning models in production environments by coollaborating with DEVOPS team

• Leverage MLFlow for tracking experiments, managing model versions, and handling model deployment and registry.
• Develop and maintain scalable and efficient Kubernetes-based infrastructure for model training, versioning, and deployment.
• Collaborate with data scientists and ML engineers to design and implement reproducible ML pipelines using MLFlow.
• Maintain ML Models on Kubernetes clusters for large-scale, distributed training and deployment.
• Implement CI/CD pipelines for machine learning models, ensuring smooth and automated deployment workflows by collabaring with DEVOPS teams with Sahibinden practices.
• Ensure compliance with best practices in model versioning, deployment, and monitoring in line with MLOps principles.

Preferred Skills:

• Experience with model monitoring tools (e.g., Prometheus, Grafana) for production systems.
• Knowledge of serverless architectures and managed Kubernetes services (e.g., GKE, EKS).
• Familiarity with additional MLOps tools such as Kubeflow, TensorFlow Extended (TFX).
• Understanding of best practices for model explainability, interpretability, and compliance.

What We Offer

• Birthday leave
• One week accommodation at Richmond Efes and Pamukkale Hotels (for employees who have completed 3 years at Sahibinden.com)
• Well-being opportunities;
• Quality life package
• Gym and Dietician service