Senior Machine Learning Engineer @ AVENGA in AVENGA

As a Machine Learning Engineer, you will collaborate with data scientists, product managers, and data engineers to operationalize machine learning models in production and manage the lifecycle of artificial intelligence algorithms on a variety of domains. You will develop and deploy novel approaches to optimize existing machine learning systems to maximize their business value. Work for 8 hours with so-called overlap from 16:00 -20:00 CET.

Basic Qualifications

• Minimum of seven years’ post-secondary education or relevant work experience

Other Required Qualifications:

• Minimum of seven years’ post-secondary education or relevant work experience
• Bachelor's degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline.
• Minimum of five years’ software development experience with Python and SQL.
• Minimum of three years’ experience in developing and deploying machine learning systems into production in a cloud environment.
• Minimum of two years’ experience testing, maintaining, or launching software products and minimum of one year of experience with software design and architecture.
• Experience working with a variety of relational SQL and NoSQL databases, big data
  Tools: Hadoop, Spark, Kafka; a Linux environment; and at least one cloud provider solution (AWS, GCP, Azure).
• Knowledge of data pipeline and workflow management tools.
• Expertise in standard software engineering methodology, e.g., unit testing, test automation, continuous integration, code reviews, design documentation.

Other Preferred Qualifications:

• Experience with neural networks, deep learning, and reinforcement learning, using frameworks such as TensorFlow.
• Experience with Natural Language Processing (NLP), Large Language Models (LLMs), and/or Recommendation Engines.
• Relevant working experience with Docker and Kubernetes.
  

As a Machine Learning Engineer, you will collaborate with data scientists, product managers, and data engineers to operationalize machine learning models in production and manage the lifecycle of artificial intelligence algorithms on a variety of domains. You will develop and deploy novel approaches to optimize existing machine learning systems to maximize their business value. Work for 8 hours with so-called overlap from 16:00 -20:00 CET.

,[Architect, build, maintain, and improve new and existing suite of algorithms and their underlying systems., Automate machine learning pipelines and monitor and optimize machine learning solutions., Implement end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance tuning, and A/B testing., Use your entrepreneurial spirit to identify new opportunities to optimize business processes, improve consumer experiences, and prototype solutions to demonstrate value., Work closely with data scientists and analysts to create and deploy new product features online and in mobile apps., Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation., Write efficient and well-organized software to ship products in an iterative, continual-release environment., Contribute to and promote good software engineering practices across the team., Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code., Actively contribute to and re-use community best practices., Monitor, debug, track, and resolve production issues., Work with project managers to ensure that projects proceed on time and on budget., Collaborate with Technical Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact. Complete other responsibilities as assigned.]

Requirements: Python, SQL, Machine learning, NoSQL, Big Data, Hadoop, Spark, Kafka, Linux, cloud, TensorFlow, Natural language processing, Language Models, Docker, Kubernetes, GCP, Azure, AWS
Tools: .
Additionally: Sport subscription, Private healthcare, Flat structure, International projects, Small teams, LuxMED, Multisport, Integration events.