AI Scientist

Key Responsibilities

• Lead the end-to-end prototyping lifecycle for data and AI systems, from concept design to Proof-of-Concept development.

• Develop and refine AI prototypes using novel techniques, with emphasis on LLM development, fine-tuning, optimisation, and real-world integration.

• Produce clear, detailed technical documentation for experiments and prototypes.

• Support and run innovation challenge activities to surface new ideas and prototype approaches.

• Contribute technical thought leadership through papers, blogs, and presentations on AI best practices.

• Participate in demos, technical showcases, and internal knowledge-sharing sessions.

• Engage with global research and industry partners to stay current with emerging trends and represent the team at international forums.

Requirements

You should have at least 5 years of experience in two or more of the following domains:

AI Product Engineering / Prototyping

• Translating real-world problems into AI/ML problem statements.

• Building ML/LLM models using modern AI libraries and techniques.

• Deploying AI workflows to enhance product development or operational processes.

• Assessing the effectiveness, scalability, and sustainability of AI workflows.

Data Engineering

• Hands-on experience with data collection, ingestion, storage, transformation, and pipeline design.

• Ability to map complex data sources, design data flows, and integrate multiple streams into usable datasets.

• Familiarity with data management and optimisation practices.

AI Ethics & Governance

• Applying ethical guidelines and governance frameworks in AI development.

• Ensuring responsible and compliant use of AI in projects.

Additional Attributes

• Experience commercialising or translating emerging technologies into practical applications.

• Strong understanding of the practical limitations and challenges in AI and data systems.

• Passion for hands-on experimentation and rapid prototyping.

• Interest or experience in LLM development, model fine-tuning, agentic workflows, or generative AI applications.

• Strategy & Leadership
• Define and drive the organization’s vision and strategy for generative AI, ensuring alignment with overall business objectives.
• Identify opportunities for innovation and business value creation through AI-driven insights and solutions.
• Develop and maintain a roadmap for AI adoption, piloting initiatives, and driving adoption across various business units.
• Solution Development & Implementation
• Lead cross-functional teams of data scientists, engineers, product managers, and domain experts to design, build, and deploy generative AI-driven solutions that address key business challenges.
• Develop and maintain an enterprise-wide Generative AI platform, ensuring it is scalable, secure, compliant, and easily accessible to different business units.
• Oversee large-scale AI projects, ensuring technical feasibility, budget management, and timely delivery.
• Guide the development of proof-of-concepts (PoCs) and MVPs, creating scalable AI solutions that can be integrated into the organization’s technology stack.
• Team Building & Management
• Attract, hire, and retain top AI talent, developing an environment that fosters creativity, collaboration, and continuous learning.
• Coach and mentor team members, providing professional development opportunities and skill-building initiatives.
• Establish agile project management practices, promoting high efficiency, accountability, and transparency.
• Stakeholder Engagement
• Collaborate with senior leadership, department heads, and external partners to identify business needs and evaluate AI solution viability.
• Communicate complex AI concepts in clear, business-oriented language to stakeholders at all levels.
• Build and maintain relationships with technology vendors, business lines and other external parties for potential partnerships and co-innovation projects.
• Governance & Ethics
• Define and implement best practices for AI ethics, data privacy, and responsible AI governance.
• Ensure compliance with relevant regulations and internal policies.
• Establish standards and guidelines for model deployment, monitoring, and risk management.
• Performance Measurement & Continuous Improvement
• Develop KPIs and metrics to measure the success, impact, and ROI of AI initiatives.
• Implement processes for ongoing evaluation and optimization of AI models and processes.
• Cultivate a data-driven culture and champion continuous improvement across all AI-related activities.
  
   

• Education
  • Background in computer science, Machine Learning, Data Science, or a related field preferred. Equivalent experience in AI leadership and innovation may be considered.
• Experience
  • 10+ years of experience in AI/ML, data science, or a similar field, with at least 5 years in a leadership role.
  • Proven track record of delivering AI solutions (particularly generative models) in complex, high-impact environments.
  • Experience managing cross-functional teams and large-scale, transformational projects.
• Technical Expertise
  • Proficiency in machine learning, deep learning, NLP, computer vision, and other AI domains.
  • Familiarity with the latest generative AI architectures (e.g., Transformer-based models, diffusion models, etc.) and large-language-model frameworks.
  • Experience with cloud-based AI platforms (AWS, Azure, GCP) and relevant ML frameworks (TensorFlow, PyTorch, etc.).
  • Demonstrated experience in building or integrating AI platforms in an enterprise setting.
• Business Acumen
  • Strong understanding of how AI can drive business value, with the ability to connect technical capabilities to strategic objectives.
  • Experience in budgeting, project planning, and managing vendor relationships.
  • Adept at quantifying business impact and ROI of AI initiatives.
• Leadership & Soft Skills
  • Exceptional communication skills, capable of translating complex technical topics into business language.
  • Demonstrated ability to lead, motivate, and mentor high-performing teams.
  • Strong problem-solving and decision-making abilities with a track record of managing ambiguity.
  • Collaborative mindset, capable of building consensus among diverse stakeholders.

• Strategic Thinking: Ability to set a clear vision for AI initiatives that align with overall business goals.
• Innovation & Creativity: Comfortable exploring novel approaches and challenging the status quo.
• Execution Excellence: Track record of taking AI concepts to production, with a focus on quality and scalability.
• Change Management: Skilled in driving organizational change, influencing culture shifts, and instilling a data-driven mindset.
• Risk Management: Adept at identifying and mitigating technical, ethical, and compliance-related risks.
• Collaboration & Influence: Strong interpersonal skills to forge partnerships with internal and external stakeholders.