AI/ML Engineer

pALTIA is a multidisciplinary team focused on delivering advanced data-driven solutions that generate measurable impact and enable smarter decision-making. Working across international environments, we combine analytics, artificial intelligence, and business expertise to transform data into strategic value. Our mission is to help organizations evolve through innovation, scalable data platforms, and high-quality analytical solutions, fostering continuous learning, collaboration, and technological excellence. /ppWe are looking for a Data Scientist AI Specialist with strong expertise in machine learning, Natural Language Processing (NLP), generative AI (LLMs/RAG), and data engineering pipelines applied to environmental footprints, life-cycle assessment (LCA), and sustainability analytics to join our team in international digital transformation and scientific data management initiatives. /ppThe role involves designing, training, and deploying ML, NLP, and generative AI models (including local/API-based LLMs, LangChain, LlamaIndex, and Retrieval-Augmented Generation / RAG). The specialist will build and optimise scalable data pipelines to process, clean, validate, and convert heterogeneous datasets, such as official statistics, trade data, and life-cycle inventory (LCI) databases. Additionally, the candidate will focus on implementing advanced time-series forecasting, nowcasting, and data-imputation techniques to fill missing data gaps, establishing automated anomaly detection, and ensuring strict compliance with the EU AI Act, GDPR, and AI safety principles (transparency, bias mitigation, and explainability). /ppIn close collaboration with scientific project leads, policy officers, and technical engineering teams, the specialist will translate complex environmental and business needs into working prototypes and technical specifications. The position requires deep expertise in Python (pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow), relational databases (PostgreSQL/SQL), GPU optimisation, containerisation, and reproducible model packaging using Git and Docker. /ph3WHAT WE HAVE FOR YOU? /h3ulliRemote work model (far-site delivery with occasional presence in European locations if required). /liliFlexible

working hours
. /liliLong-term stable position with executive-level professional growth opportunities. /liliContinuous learning and upskilling in cutting-edge Generative AI, RAG architectures, MLOps, and sustainability analytics. /liliIntegration into an experienced, highly skilled, and collaborative international team. /liliParticipation in high-impact international digital and AI projects. /liliInclusive and innovation-driven work environment. /li /ulh3REQUIREMENTS: /h3h3Educational level

Experience:
/h3ulliEQF Level 7 (Master's degree or 5 years of higher education). /liliMinimum 5 years of professional IT experience. /liliMinimum 5 years of hands-on experience in Python for data processing and analysis (pandas, NumPy, SciPy, etc.). /liliMinimum 5 years of experience developing ML/AI models and applications (scikit-learn, TensorFlow, PyTorch, Hugging Face, etc.). /liliMinimum 2 years of specific experience in identifying, converting, updating, and assessing the quality of datasets for Life-Cycle Assessment (LCA) or environmental footprint modelling (Product Environmental Footprint / PEF, Organisation Environmental Footprint / OEF). /li /ulh3Languages: /h3ulliMinimum B2 level in English (spoken and written). /li /ulh3TECHNICAL FUNCTIONAL

EXPERIENCE:
/h3ulliAI Machine Learning: Supervised/unsupervised algorithms (SVM, Neural Networks, Decision Forests, k-NN), NLP (SpaCy, NLTK), feature selection, classifier optimisation, and model interpretability. /liliGenerative AI RAG: Large Language Models (LLMs), small language models, generative AI APIs, Retrieval-Augmented Generation (RAG) architectures, and orchestration using LangChain, LlamaIndex, and Hugging Face. /liliAdvanced Analytics Data Imputation: Time series analysis, forecasting, nowcasting, statistical testing, and gap-filling techniques for incomplete datasets. /liliEnvironmental Sustainability Modelling: Processing heterogeneous trade, production, consumption, and LCI/LCIA data models, environmental indicators, and supply chain footprints. /liliData Quality Governance: Data cleansing, verification, automated anomaly detection, data freshness/accuracy, metadata, master data management, bias mitigation, and compliance with the EU AI Act GDPR. /liliMLOps Architecture: Technical architecture design, parallel processing, GPU usage, containerisation, version control (Git), dependency management, and reproducible pipelines. /li /ulh3TECHNOLOGIES

SKILLS:
/h3ulliProgramming Libraries: Python (pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly), R, SQL. /liliML/AI Frameworks: Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, NLTK, LangChain, LlamaIndex. /liliDatabases Storage: PostgreSQL, relational databases, data lake/lakehouse concepts. /liliDevOps To