Established in 1997, Softeq was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together!

Locations/type of contract:

EU country (B2B contract directly with Softeq, fully remote)

We are looking for an experienced and motivated engineer to join our Data Science team within Medication Delivery Solutions (MDS) Business as a Senior MLOps Engineer. As an MLOps Engineer, you will play a crucial role in the deployment, monitoring, and maintenance of machine learning models. You will work closely with data scientists, software engineers, and IT operations to ensure that our machine learning models are reliable, scalable, and performing optimally in production environments. Your expertise will be essential in automating and streamlining our ML workflows, enhancing model reproducibility, and ensuring continuous integration and delivery. The MLOps Engineer will directly report to the Director of Data Science. 

Responsibilities: 

• Design, build, and maintain the infrastructure required for efficient development, deployment, and monitoring of machine learning models. 

• Implement CI/CD pipelines for machine learning applications. 

• Develop and manage cloud-based and on-premises solutions for model training, deployment, and monitoring. 

• Ensure the scalability, reliability, and performance of machine learning systems. 

• Collaborate with data scientists to understand and implement requirements for model serving, versioning, and reproducibility. 

• Monitor and optimize model performance in production, identifying and resolving issues proactively. 

• Automate repetitive tasks to improve efficiency and reduce the risk of human error. 

• Maintain documentation and provide training to team members on MLOps best practices. 

• Stay updated with the latest developments in MLOps tools, technologies, and methodologies. 

• Communicate and share knowledge with other team members and actively participate in various learning-sharing opportunities 

Requirements: 

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 

• 3+ years of experience in MLOps, DevOps, or related fields. 

• Strong programming skills in Python, with experience in other languages such as Java, C++, or Scala being a plus. 

• Experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn. 

• Proficiency with CI/CD tools such as Jenkins, or GitLab CI. 

• Hands-on experience with AWS cloud platform (Google Cloud or Azure are nice to have)

• Familiarity with containerization and orchestration tools like Docker and Kubernetes. 

• Knowledge of infrastructure-as-code tools such as Terraform or CloudFormation. 

• Strong understanding of machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment. 

• Excellent problem-solving skills and the ability to work independently as well as part of a team. 

• Strong communication skills and the ability to explain complex technical concepts to non- technical stakeholders. 

• Very good English language skills, both written and verbal (min. B2)

Preferred Qualifications: 

• Experience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon. 

• Familiarity with data engineering tools like Apache Spark, Kafka, or Airflow. 

• Knowledge of security best practices for machine learning systems. 

• Experience with A/B testing and model performance monitoring