Senior AI & NLP Engineer - Hybrid

Candidates need to be fluent in English. A work permit is required, but not provided. This position is open for contractors.

• Tasks and Responsibilities:
• 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 ML techniques;
• Develops aiming at improving the quality of machine translation (MT) engines for each installed language pair;
• 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;
• Provision of expert advice and assistance in any area associated with the AI and Data Warehouse technologies;
• Perform design, development and maintenance of BI and reporting software that both follow the reference architecture rules and accurately implement the specifications;
• Assistance and support to the Advanced Analytics developers;
• Provision of technical studies, technical expertise, technical evaluations in relation with AI systems

• Master degree;

+9 years of IT experience;

• +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 LLMAPIs;
• 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);
• +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;
• +1 year of experience in:
• Working with unstructured data, non-relational databases, and data lakes;
• Fluent in English