We are looking for an innovative and passionate Machine Learning Engineer to join our fast-paced, dynamic team. 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.

Key Responsibilities:

• 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.

Requirements

What are you bringing to the team:

• 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.

Nice to have:

• Experience with big data technologies (e.g., Hadoop, Spark).
• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Knowledge of DevOps practices and tools.

Benefits

Competitive salary (open to negotiation) with performance-based bonuses.

Professional growth opportunities in a fast-growing startup.

Flexible working hours and remote work options.