JOB OFFERS

AI Engineer

ALTEC is involved in several Science and Technological Programs in the aerospace domain where data systems are represented by software components that together with infrastructure and communication system constitute the ground segment of a space mission. Data systems are essential as are they represent reliable and efficient performant software that is needed to provide ground segment services and execute mission operations.

The Data Processing System and Scientific and Technological Application group hosts the engineers, physicists and mathematicians who oversee the space mission data systems from definition to end of operations; it develops and maintains the data systems aimed at mission support and the execution of science operations on ground; it archives and manages their data during operations and beyond. Moreover, it is involved in the post-mission data exploitation in order to develop applications aimed at maximizing the scientific and technological return of value devoted to maximize the information value extracted from such data.

Duties

You will be part of the team that is in charge of all engineering aspects of the development, implementation and maintenance of software data systems under the ALTEC responsibility.

You will work in close collaboration with the data engineers, software engineers, mission operators and infrastructure engineer during project phases.

You will be in charge of AI application definition, design, training, validation and documentation.

You will be expected to implement innovative applications based on artificial intelligence technologies within the aerospace domain.

REQUIRED EXPERIENCE & TECHNICAL SKILLS

  • A MSc or equivalent degree in software or data science engineering with high score.
  • Practical experience designing, training, validating, and deploying Machine Learning and Deep Learning models.
  • Experience with supervised, unsupervised and self-supervised paradigms. Solid understanding of deep model architectures (e.g., CNNs, RNNs, Transformers).
  • Experience with model, dataset and experiment versioning.
  • Experience with software development leveraging the Python language.
  • Experience designing and implementing data pipelines handling structured and unstructured data, leveraging data frameworks and libraries (i.e. NumPy, Pandas, SciPy) and managing metadata.
  • Experience with the most popular AI frameworks PyTorch, TensorFlow and Scikit-learn for Machine Learning and Deep Learning models.
  • Understanding of model evaluation methodologies, cross-validation strategies, hyperparameter optimization (e.g., Optuna), and performance metrics for classification, regression and ranking tasks.
  • Knowledge of MLOps approach and tools to implement CI/CD pipeline for AI workflows.
  • Ability to use query language for both relational database and NoSQL databases.
  • Ability to use of the Linux operating system and scripting languages.
  • Ability to use debugging tools, perform troubleshooting and profiling an application.

 

SOFT SKILLS

  • Problem Solving
  • Result oriented
  • Operational efficiency
  • Fostering Cooperation
  • Relationship Management
  • Continuous Improvement

 

ADDITIONAL ASSETS (not mandatory, considered as a plus)

  • Experience in Agile methodologies would be a further asset.
  • Knowledge of container technology and deployment on Kubernetes.
  • Knowledge of distributed data processing frameworks, such as Apache Spark.
  • Knowledge of ONNX for model optimization and interoperability.
  • Knowledge of foundation models and LLMs and their integration into applications using APIs or open-source models.
  • Knowledge of Generative AI application patterns such as RAG, prompt engineering and evaluation of LLM based systems.
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