Job Description
Your primary focus will be contributing to data-driven solutions and ML deployment to support Additive Manufacturing. As an MLOps engineer, you will play a key role in developing more adapted ML pipelines for domain applications. Furthermore, you will be contributing to standardise the processes for feature engineering, model training and experimentation. You will have the opportunity to work with state-of-the-art hardware and needed infrastructure for the development of AI/ML-based solutions.
For this role, prior working experience is required. If you are eager to push the
boundaries of what we can do with ML and thrive in a collaborative, innovative
environment, we’d love to hear from you!

REQUIRED SKILLS AND QUALIFICATIONS

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  Highly motivated to learn about the aerospace domain and come up with new ideas to develop ML and data-driven solutions.
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  Strong ML/AI (advanced deep learning and sequence models for computer vision tasks) expertise and minimum 3 years of working experience.
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  Minimum 3 years working experience with model registries, training, logging (MLOps)
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  Working experience with real-time ML models, ML pipelines, and ML libraries, model serving.
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  Working experience with bringing ML models to production
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  Working experience with monitoring performance (Data drift monitoring
• 
  Full stack development skills that can help to build products from end to end (Python, Node.js, React, FastAPI etc.)
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  Working experience with Docker and Kubernetes.
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  Working experience with databases (Postgres).
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  Able to work with both Windows and Linux environments.
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  Agile development experience.
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  Experience in shop floor AI, factory AI, and industrial AI.
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  Having additive manufacture

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  Design, develop and evaluate ML/AI (both supervised and unsupervised learning for computer vision tasks) models for focused domain application.
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  Standardize process for feature engineering and model experimentation
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  Develop ML pipelines that suit domain applications and production.
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  Deploy models for real-time monitoring and data drift monitoring
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  Collaborate with teams to report findings, and collect feedback to improve ML technologies.
• 
  Communicate verbally and writing detailed analysis of models.
  Requirements