Artificial Intelligence Graduate

Requirements: Solid understanding of foundational AI and machine learning concepts, including supervised and unsupervised learning, classification, regression, clustering, and model evaluation. Familiarity with algorithms such as linear regression, logistic regression, decision trees, support vector machines, neural networks, and ensemble methods. Proficiency in programming languages commonly used in AI/ML, such as Python or R, along with experience using relevant libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Knowledge of C# and .Net would be an advantage. Knowledge of data preprocessing techniques, feature selection, and dimensionality reduction. Basic experience with data visualization tools and techniques to communicate insights from data. Understanding of deep learning architectures particularly transformer models, and foundational knowledge of Large Language Models (LLMs) and generative AI concepts. Familiarity with prompt engineering techniques for interacting with generative AI models and basic knowledge of RAG (Retrieval Augmented Generation) architectures. Awareness of emerging AI interoperability standards, such as Model Context Protocol (MCP) and Agent2Agent (A2A), and their role in building interconnected AI systems. Ability to work with version control tools such as Git, and familiarity with collaborative development practices. Strong problem-solving skills, analytical thinking, and attention to detail. Excellent written and verbal communication skills. Responsibilities: Collaborate with cross-functional teams to design, develop, and deploy machine learning models and AI solutions for real-world problems. Assist in data collection, preprocessing, and exploratory data analysis to ensure high-quality datasets for model training and evaluation. Implement, test, and optimize machine learning algorithms using relevant libraries and frameworks such as TensorFlow, PyTorch, or scikit-learn. Contribute to the development and refinement of AI pipelines, including data ingestion, feature engineering, model selection, and validation. Conduct literature reviews to stay updated on the latest trends, techniques, and developments in the field of AI and machine learning. Document technical processes, experiments, and results to facilitate knowledge sharing and reproducibility within the team. Participate in code reviews, brainstorming sessions, and team meetings to foster a collaborative and innovative working environment. Present findings, analyses, and results effectively to both technical and non-technical stakeholders.

AVEVA provides industrial software aimed at helping various industries, such as manufacturing, energy, and infrastructure, undergo digital transformation. Their main product, the CONNECT platform, is an open industrial intelligence platform that allows businesses to enhance their operations through cloud-based solutions. This platform is designed to be hardware-agnostic, meaning it can function independently of Schneider Electric's hardware, which broadens its appeal to a wider market. AVEVA has shifted to a subscription-based service model, offering software-as-a-service (SaaS) for engineering asset management, making their solutions more flexible and accessible. Additionally, the company offers online training to assist clients and partners in adapting to this new model.