We are seeking an experienced AI and ML Ops Engineer (m/f/d) with a strong development background to elevate our AI and machine learning projects to the next level. In this role, you will be responsible for the deployment, scaling, and optimization of machine learning models in production environments. If you are an expert in Python, possess experience with additional programming languages, and feel at home in the world of AI and MLOps, we are looking forward to receiving your application!
Your Responsibilities:
Building a central AI platform: Develop a unified AI platform for the Viega Group that supports both genAI and classical AI use cases.
Developing and operating ML pipelines: Build, automate, and optimize end-to-end ML pipelines (data preparation, training, validation, deployment).
Model deployment: Implement and scale machine learning models in production environments (e.g., using Docker, Kubernetes, or cloud platforms like AWS/GCP/Azure).
Monitoring and maintenance: Ensure the stability and performance of ML models post-deployment (monitoring, logging, debugging).
Collaboration with Data Scientists: Work closely with data scientists in developing and operationalizing models.
Code optimization: Write efficient, scalable, and maintainable code in Python as well as other programming languages.
CI/CD for ML: Build and maintain continuous integration/continuous deployment (CI/CD) pipelines for machine learning projects.
Ensuring reproducibility: Implement best practices for versioning (e.g., data, code, and model versioning using tools like DVC or MLflow).
Research & Innovation: Evaluate new technologies, frameworks, and tools in the field of AI/MLOps.
requirements-expected :
Must-Have Skills:
Development background: Several years of experience in software development with a focus on backend systems or data applications.
Programming skills: Excellent knowledge of Python as well as experience with at least one additional programming language (e.g., Java, C++, Go, or Scala).
MLOps experience: Practical experience with MLOps principles and tools such as MLflow, Kubeflow, Airflow, or Prefect.
Cloud expertise: Experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and their AI/ML services.
Containerization & orchestration: Proficiency in Docker and Kubernetes for deploying scalable applications.
Database knowledge: Experience with relational databases (e.g., PostgreSQL) as well as NoSQL databases (e.g., MongoDB, vector databases).
