Machine Learning Engineer, Oppizi

• Education: Bachelor's or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field
• Experience: Minimum of 4 years of experience as a Machine Learning Engineer or in a similar role
• MLOps Expertise: Proven experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended)
• Programming Skills: Strong proficiency in Python and experience with libraries such as Pandas, NumPy, and Scikit-learn
• Cloud & Containerization: Hands-on experience with cloud platforms like AWS, Azure, or Google Cloud, and familiarity with containerization technologies like Docker and Kubernetes
• Data & API Proficiency: Experience with data manipulation and building APIs using frameworks such as FastAPI, Flask, or Django
• Communication Skills: Ability to explain complex technical concepts to both technical and non-technical stakeholders

Desirable

• As a key member of our technology division, you will play a vital role in developing, deploying, and optimizing end-to-end machine learning models
• This role demands expertise in MLOps (Machine Learning Operations) and the ability to handle the entire machine learning lifecycle—from data ingestion to model deployment and beyond
• As a Machine Learning Engineer at Oppizi, you will have the opportunity to collaborate with cross-functional teams, including software engineers and product managers, to deliver high-quality ML solutions that directly impact business outcomes
• You will also be responsible for ensuring that our models are scalable, efficient, and aligned with the company’s goals
• Model Development and Deployment: Design, implement, and deploy machine learning models that address business needs, ensuring high availability and performance in production environments
• MLOps and Automation: Apply MLOps best practices to automate the ML lifecycle, including data ingestion, training, and deployment pipelines. Build and maintain CI/CD pipelines for continuous integration and delivery
• Performance Monitoring and Optimization: Monitor deployed models to ensure they meet performance metrics, and continuously improve them for accuracy and scalability
• Collaboration and Communication: Work closely with software engineers, product managers, and other stakeholders to develop and communicate ML solutions effectively