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- Aufgaben: Build and maintain ML infrastructure, prepare datasets, and deploy models seamlessly.
- Arbeitgeber: Join a forward-thinking company focused on innovative machine learning solutions.
- Mitarbeitervorteile: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Warum dieser Job: Be part of a dynamic team shaping the future of AI with impactful projects.
- Gewünschte Qualifikationen: Proven MLOps experience, strong Python/C++ skills, and familiarity with CI/CD tools required.
- Andere Informationen: Work with cutting-edge technologies like AWS and Terraform to enhance scalability.
Das voraussichtliche Gehalt liegt zwischen 48000 - 84000 € pro Jahr.
Position Description As a (Senior) Machine Learning Engineer, you will play a crucial role in building and maintaining the infrastructure and processes required to support machine learning operations. You will be responsible for preparing datasets, evaluating machine learning models using key performance indicators (KPIs), validating and deploying models, and ensuring their seamless integration into production systems (embedded and Cloud). Additionally, you will design and implement CI/CD pipelines for machine learning, automate ML operations, and utilize cloud-based solutions, such as AWS with Terraform, to enhance scalability and efficiency. This position requires a proactive individual with a strong foundation in MLOps practices, cloud platforms, and automation tools. In this role, you will: Prepare and analyze datasets to support machine learning model development and training Assess natural language processing and computer vision models using relevant KPIs and metrics Validate models to ensure they meet performance standards and align with project requirements Deploy machine learning models into production environments with scalability and reliability Continuously monitor deployed models to ensure optimal performance and address issues as they arise Design, develop, and maintain CI/CD pipelines to streamline ML model development and deployment workflows Automate repetitive and manual processes involved in machine learning operations to improve efficiency Implement and manage MLOps solutions on AWS, leveraging Terraform for infrastructure as code What you will need to succeed: Several years of proven experience in MLOps, including end-to-end machine learning lifecycle management Strong programming skills in Python and C++ Familiarity with MLOps tools like MLFlow, Airflow, or Kubeflow. Experience designing and managing CI/CD pipelines for machine learning projects with experience in CI/CD tools (e.g., Jenkins) Proficiency in automation tools for streamlining ML operation Experience in Natural Language Processing (NLP) and Computer Vision (CV), workflows and metrics Hands-on experience in model validation, testing, and deployment to production Strong verbal and written communication skills in English
(Senior) Machine Learning Engineer (f/m/d) Arbeitgeber: Cinemo GmbH
Kontaktperson:
Cinemo GmbH HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: (Senior) Machine Learning Engineer (f/m/d)
✨Tip Number 1
Make sure to showcase your hands-on experience with MLOps tools like MLFlow, Airflow, or Kubeflow during the interview. Be prepared to discuss specific projects where you implemented these tools and the impact they had on your workflow.
✨Tip Number 2
Familiarize yourself with AWS and Terraform, as these are crucial for the role. Consider working on a personal project that involves deploying a machine learning model using these technologies to demonstrate your practical knowledge.
✨Tip Number 3
Prepare to discuss your experience with CI/CD pipelines in detail. Think of examples where you designed or managed these pipelines, and be ready to explain how they improved the efficiency of your machine learning projects.
✨Tip Number 4
Since communication skills are emphasized, practice explaining complex technical concepts in simple terms. This will help you convey your ideas clearly during the interview and show that you can collaborate effectively with non-technical team members.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: (Senior) Machine Learning Engineer (f/m/d)
Tipps für deine Bewerbung 🫡
Highlight Relevant Experience: Make sure to emphasize your experience in MLOps and the end-to-end machine learning lifecycle. Detail specific projects where you prepared datasets, validated models, or deployed them into production environments.
Showcase Technical Skills: Clearly list your programming skills in Python and C++, as well as your familiarity with MLOps tools like MLFlow, Airflow, or Kubeflow. Mention any experience with CI/CD tools such as Jenkins and automation tools that you've used.
Demonstrate Problem-Solving Abilities: Provide examples of how you've addressed issues in deployed models or improved efficiency in ML operations. This will showcase your proactive approach and ability to handle challenges.
Tailor Your Application: Customize your CV and cover letter to align with the job description. Use keywords from the job listing, such as 'cloud-based solutions', 'Terraform', and 'monitoring deployed models' to make your application stand out.
Wie du dich auf ein Vorstellungsgespräch bei Cinemo GmbH vorbereitest
✨Showcase Your MLOps Experience
Be prepared to discuss your previous experience with the end-to-end machine learning lifecycle. Highlight specific projects where you managed MLOps processes, focusing on how you prepared datasets, validated models, and deployed them into production.
✨Demonstrate Technical Proficiency
Make sure to showcase your programming skills in Python and C++. Be ready to provide examples of how you've used these languages in your past work, especially in relation to automation tools and CI/CD pipelines.
✨Familiarity with Tools is Key
Discuss your experience with MLOps tools like MLFlow, Airflow, or Kubeflow. If you have worked with CI/CD tools such as Jenkins, be sure to mention specific instances where you designed and managed pipelines for machine learning projects.
✨Prepare for Technical Questions
Expect technical questions related to Natural Language Processing (NLP) and Computer Vision (CV). Brush up on relevant KPIs and metrics, and be ready to explain how you would assess and validate models in these areas.
