AI Engineer

Roles & Responsibilities

In a world of technological disruptions, the role of our Data Science team is to develop analytical, artificial intelligence and technological solutions to enhance delivery of our client service and augment our professional service offerings.

As an AI Engineer, you will support and lead the team in various initiatives:

• Building and deploying AI solutions that solve real business problems and streamline internal processes.
• Designing, fine-tuning, and evaluating LLMs and other machine learning models using internal data and secure infrastructure.
• Integrating AI capabilities into internal systems and exploring opportunities with commercial AI services where relevant.
• Collaborating with cross-functional teams (e.g. process owners, IT and governance) to ensure AI initiatives are aligned with business needs, secure, and scalable.
• Researching and experimenting with agentic AI tools, both open-source and commercial, to bring it to practical use.
• Mentoring junior team members and playing a key role in AI capability building

Your profile

As someone who is passionate in analytics and technology, you are eager to create impactful solutions to address business challenges. You like to work in a close-knit collaborative team environment where your ideas are valued. You also thrive on difficult challenges and look forward to learning and growing professionally with the team.

Requirements:

• Degree in Data Science, Computer Science, Business Analytics, Statistics, Mathematics, or a related field
• Experience in building and deploying AI/ML models (including LLMs)
• Driven, inquisitive, and detail-oriented
• Excellent communication skills
• Ability to work under pressure and meet tight deadlines
• Strong desire to learn and to make an impact
• More than 3 years of experience in building and deploying AI/ML models in production (including AI agents)

Technical skills:

• Proficient in programming or scripting (e.g. Python)
• Familiar with ML libraries and frameworks (e.g. PyTorch/TensorFlow, LangChain/LangGraph, etc.)
• Exposure to cloud platforms (e.g. Azure)
• Understanding of data security, governance and model evaluation techniques