Overview
In this Lead GenAI System Architect role, you will shape and deliver Generative AI and ML-driven programs for major clients. You’ll design scalable AI pipelines, oversee advanced models, and mentor cross-functional teams within Deloitte’s AI Institute. The position sits at the intersection of business impact and cutting-edge tech, offering exposure to diverse, international clients and high-growth AI initiatives. You will contribute to responsible, secure deployment while staying ahead of AI research and industry developments.
Pay / Benefits
- hybrid working in London
- development and learning opportunities
- supportive wellbeing culture
- purpose-driven culture
- collaborative environment
- flexible working arrangements
Responsibilities
- Engage with senior client stakeholders and leadership to shape AI strategies aligned with business goals
- Lead design, development, and deployment of advanced AI pipelines including data prep, feature engineering, model development, evaluation, and secure deployment at scale
- Oversee development and operationalization of state-of-the-art AI models (LLMs, diffusion models) ensuring scalability and robustness
- Stay abreast of AI Generative AI research and integrate relevant advances into client solutions and internal frameworks
- Mentor cross-functional AI teams, promoting knowledge sharing and reusable assets to drive delivery excellence
Key requirements
- PhD in Computer Science, Machine Learning, Artificial Intelligence, or related discipline (preferred)
- Extensive experience designing, developing, and deploying enterprise-grade AI/ML solutions with stakeholder management
- Deep domain expertise applying AI/Generative AI in regulated or data-rich industries
- Track record of thought leadership in AI/ML (patents, publications, or open-source contributions)
- Industry certifications (AWS/Google/Azure/IBM ML) and relevant technical coursework knowledge
- Stakeholder management
- Excellent communication
- Mentoring and leadership
- Python expert; PyTorch, TensorFlow
- Generative AI libraries (LangChain, LangGraph)
- LLMs, prompt engineering, RAG pipelines, vector databases
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