AI & NLP Engineer

Role Overview

• Development and maintenance of software applications in the field of Natural Language Processing (NLP), Machine Learning (ML) and/or Artificial Intelligence (AI);
• Training of custom machine learning / deep learning models based on structured and unstructured data;
• Selecting features, building and optimizing classifiers using machine learning techniques;
• Studies and developments aiming at improving the quality of machine translation (MT) engines for each installed language pair, addressing the specific needs of customers of the service concerning MT quality and contributing to a general strategy for the systematic evaluation and long-term improvement of MT quality;
• Interact with data stewards and other IT stakeholders to define the data rules;
• Define data controls and implement actions to ensure data quality and integrity;
• Creating automated anomaly detection systems and constant tracking of its performance;
• Data mining using state-of-the-art methods;
• Processing, cleansing, and verifying the integrity of data used for analysis;
• Design the IT architecture for solitons in the NLP / ML / AI fields, and coordinate its implementation considering master
• and meta-data management concepts;
• Analyse data architecture for consistency, completeness, accuracy and reasonableness;
• Contributing for the analysis of data management vision, strategy and policy and derive the IT requirements;
• Contributing to the design of the IT architecture considering master
• and meta-data management concepts.
• Perform risk management and act proactively against project threats.
• Manage Issues, Changes and Decisions and document them thoroughly in the project logs.
• Participate to the preparation of Business cases, Vision documents, Project Charters.
• Propose solutions by combining IT tools and business process refactoring or improvements based on an in-depth understanding of a specific business domain.
• Working with EU legislation and extracting requirements from legal documents.
• Support in transitioning from Project to Operations

Requirements

• Master + 11 years of relevant experience
• At least 3 years of experience in:
• Evaluating, comparing, selecting, and optimizing AI/ML models and architectures based on performance, operational constraints, and risk considerations.
• Applying machine-learning techniques including supervised, unsupervised, semi-supervised, and reinforcement learning (e.g. k-NN, Naive Bayes, SVM, decision forests, neural networks, and AI frameworks).
• Hands-on model evaluation, hyperparameter tuning, task-specific optimization, and resolution of model output issues.
• Developing and integrating model-serving endpoints and LLM APIs.
• Professional software development using Python, Unix/Linux, Bash, and modern development practices.
• Working with cloud platforms (AWS or Azure) and AI-related services, including ML pipelines, managed training, inference, and data storage.
• Using SQL, distributed query engines, big-data SQL, and information extraction techniques (recent experience).
• At least 2 years of experience in:
• Data management with large datasets, including data collection, selection, preprocessing, cleaning, and organization using coding and no-code tools.
• Working with NoSQL databases such as MongoDB, Cassandra, or HBase.
• At least 1 year of experience in:
• Working with unstructured data, non-relational databases, and data lakes.
• Operating in Agile development environments (experience from 2021 onward).
• Ability to understand, speak and write in English C1; French at level B1 or higher will be an advantage;
• Experience in:
• Quality assurance and quality control for machine translation (MT) and experience with MT quality procedures, testing methodologies and tools, such as automatic quality metrics (BLEU scores and similar) and human evaluation of MT quality.
• Deep learning covering common neural networks like perceptrons, feed-forward, backpropagation, and convolutional and recurrent neural networks.
• Perl, Matlab and R.
• Business-to-government definitions and related IT systems.
• DevOps practices.
• The following certifications are an asset :
• AWS Certified Machine Learning
• Microsoft Azure AI Engineer Associate
• SAS Certified Professional AI and Machine Learning Certification.
• PM2-Agile certification

Tech Stack

• AWS
• Azure
• Cassandra
• Cloud
• HBase
• Linux
• MongoDB
• NoSQL
• Perl
• Python
• SQL
• Unix