AI Technology Architect

We’re looking to grow our Responsible AI team and if you’re experienced in Technology & Architecture with a Responsible AI background, we’d love to hear from you.

As a Responsible AI Technology & Architecture professional, you’ll be experienced in building state‑of‑the‑art systems to solve difficult business challenges. But you’ll also have at least a year’s experience looking at responsible aspects such as AI model Fairness, Transparency, Explainability, Robustness, Soundness and Privacy – preferably gained in a client‑facing environment.

You’ll bring diverse expertise in architecture, software development and data engineering with specialization in RAI. You’ll be an outstanding technical specialist with the strong communication skills needed to liaise with your clients and your colleagues.

At the forefront of the industry, you’ll help make our Responsible AI vision a reality for clients looking to better serve their customers and operate always‑on enterprises. We’re not just focused on increasing revenues – our technologies and innovations are making millions of lives easier and more comfortable. But above all, we’re doing this responsibly and inclusively – to make sure AI technology is used equitably and in a way that is both ethically and technically sound.

• Lead the architecture and design of ethical AI systems, ensuring compliance with relevant regulations, organizational policies and required tools and technologies. Support client sales conversations and proposal preparation.
• Collaborate with cross‑functional teams to design integrations with existing data & AI platforms and workflows on cloud.
• Design, and guide the team on best practices and architectural guidelines for AI model development, testing, deployment, and monitoring; including helping define, build and roll out our internal Responsible AI tools and assets.
• Architect the regular audits of AI systems to identify and mitigate biases, ensuring fairness and transparency.
• Spearhead Responsible AI related technology thought leadership and PoV, staying abreast of emerging technologies.
• Provide technical leadership and mentorship to AI development teams, and develop and deliver Responsible AI technical training.
• Facilitate communication between technical and non‑technical stakeholders regarding AI projects and their impact.
• Support the evaluation of third‑party Responsible AI tools and frameworks.
• Architect and solution AI project lifecycle – from conception through to deployment and maintenance.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related technical fields.
• Manager experience: 6–8 years.
• Senior Manager: 8–10 years.
• Architectural experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and their AI services.
• Proficiency in AI and machine learning algorithms and frameworks (e.g., Tensor Flow, PyTorch).
• Strong programming skills in languages such as Python, R, or Java.
• Experience with big data technologies (e.g., Hadoop, Spark) and database management systems.
• Specific technology domain areas experience in at least one of: software development, cloud computing, systems engineering, data & analytics.
• Knowledge of ethical AI principles, data privacy laws, and regulatory requirements.
• Proven track record in leading and delivering complex AI projects.
• Experience in implementing AI solutions in large‑scale, enterprise environments.
• Strong leadership and team management skills.
• Excellent problem‑solving and analytical abilities.
• Effective communication skills, capable of conveying complex AI concepts to diverse audiences.
• Commitment to ethical AI practices and continuous learning.
• Ability to work under pressure and manage multiple projects simultaneously.
• Additional courses or training in areas related to responsible AI, ethics in technology, or similar fields are beneficial.
• Certifications in AI, machine learning, or related fields.
• Experience with Data & AI in specific domains (e.g., healthcare, finance, retail).
• Familiarity with AI model explainability and interpretability tools.
• Understanding of MLOps, LLMOps and Dev Ops practices, including CI/CD pipelines, for AI systems.
• Containerization capabilities such as Docker and Kubernetes.
• Linguistic proficiency (to a reasonable business level) in a language other than English.