Job Summary
We are looking for a Machine Learning Engineer who dares to work with cutting-edge technology! It involves inspiring yourself with state-of-the-art research papers to help with the Machine Learning model design, implementation, training and deploy. 

You will be responsible for applying the best MLOps practices and principles to the whole modeling workflow. It will require you to gain great insight into the problems, conceiving how data can be exploited and how new concepts can be created. 

Main responsibilities and duties

• Stay up-to-date with the latest advancements in machine learning by exploring and drawing insights from state-of-the-art research papers. Leverage this knowledge to design and develop innovative machine learning models tailored to our specific needs.
• Translate conceptual models into practical implementations, utilizing programming languages and machine learning frameworks. Train and fine-tune models to achieve optimal performance and accuracy.
• Drive the deployment process of machine learning models into production environments. Collaborate with cross-functional teams to ensure smooth integration and scalability of the models.
• Apply the best MLOps (Machine Learning Operations) practices and principles throughout the entire modeling workflow. Streamline processes for efficient development, testing, and deployment of machine learning solutions.
• Propose and experiment with novel ideas and approaches to tackle complex problems. 
• Collaborate with a team of skilled professionals, including data scientists, engineers, and domain experts. Foster a collaborative environment that encourages knowledge sharing and continuous learning.
  
  

Qualifications and skills

• Bachelor degree in Computer Science, Software or Electrical Engineering, or comparable professional experience in Machine Learning related areas.
• Excellent written and verbal communication skills in English.
• Experience working with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, and TFX.
• Experience in technologies like Spark, Kafka, Spark streaming, Flink etc.
• Expertise in MLOps and model integration into larger-scale applications.
• Strong programming skills in Python and experience with popular machine learning libraries/frameworks (e.g., TensorFlow, PyTorch).
• Expertise in using Docker and Kubernetes.
• Strong problem-solving skills and ability to analyze and translate business requirements into technical solutions.
• Ability to read, interpret, and apply research papers in machine learning. A strong grasp of foundational machine learning concepts is essential.
  
• Knowledge of agile methodologies. and ability to work in a fast-paced and evolving environment. Willingness to learn and adapt to new technologies and methodologies