Data Science, Artificial Intelligence & Machine Learning

Project Description:

Emerging Tech Developers create rapid proofs of concept to test hypotheses about how emerging technologies can be applied to business use cases. We learn and explore at the forefront of Artificial Intelligence, Blockchain, Internet of Things, Robotics, Virtual Reality and Augmented Reality. The lab’s role is to generate prototypes, demos and learnings that our teams and clients can use when deciding how to apply and invest in emerging technologies.

The majority of the resources would be supporting the development of assets (e.g., plugins) to further drive our overall AI strategy across the firm. In addition to that, we have ongoing AI R&D initiatives that we’d look to leverage some of these roles to help us continue driving the initiatives

Responsibilities:

• Development and training of transformer-based models for both text and images.
• Architect and oversee the entire model lifecycle, from data preparation, model design, training, development and validation to model deployment and monitoring.
• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to design and implement AI-driven solutions.
• Stay current with the latest advancements in AI and machine learning, integrating new techniques and technologies as appropriate.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Strong expertise with large scale Neural Networks, Deep Learning and Reinforcement Learning techniques.
• Practical exposure to GenAI projects and related frameworks (RAG apps, vector DBs, LangChain, LlamaIndex, agentic frameworks, ...)
• Advanced knowledge of Python and machine learning frameworks (SciPy, Scikit-learn, TensorFlow, PyTorch, pyMC, pgmpy, ...)
• Hands-on experience with one or more cloud computing platforms (Azure - preferred, AWS, GCP).
• Understanding of the whole ML lifecycle and experience with MLOps/DataOps.
• Experience with Probabilistic Graphical Modelling (Bayesian Networks, Markov Random Fields, Factor Graphs, ...)
• Strong problem-solving skills and attention to detail.
• Good communication skills, fluent English.