Lead AI Architect - Permanent - Hybrid - Insurance
Lead AI Architect - Permanent - Hybrid - Insurance
Overview
As a Lead AI Architect, you will define and drive AI architecture strategy, supporting clients to design, implement, and operate scalable platforms that enable the effective deployment of AI models. You will play a key role in shaping and delivering innovative architectures that integrate AI capabilities with existing insurance systems and digital platforms.
Key Responsibilities
Translate the vision of senior client stakeholders into an AI, ML, and GenAI architectural strategy and implementation roadmap, ensuring alignment with organisational goals and digital transformation initiatives. Provide guidance on the suitability of AI and ML initiatives and identify use cases that can scale across the organisation.
Design, recommend, and implement end-to-end architectures that integrate AI and ML solutions with existing insurance platforms and enterprise systems.
Collaborate with enterprise, application, data, and DevOps architects, along with data scientists, MLOps engineers, GenAI engineers, and business teams to design architecture and pilot use cases.
Evaluate and select appropriate technologies from open-source and commercial ecosystems, considering deployment models and integration with existing enterprise tools.
Support the successful delivery and ongoing operational improvement of AI-powered applications using agile delivery methods.
Work closely with security, governance, and risk teams to anticipate and mitigate potential risks, ensuring ethical AI implementation and compliance with emerging regulatory requirements.
Develop and maintain relationships with senior client stakeholders, contribute to proposal development, and support pricing and solution strategy.
Lead and manage diverse teams, fostering an inclusive culture where contributions are recognised and valued.
Support the development of junior team members through mentoring, on-the-job coaching, and structured development programmes.
Skills and Professional Experience
Essential
Experience in data science and an understanding of mathematical and statistical concepts relevant to AI and machine learning.
Experience implementing cloud-based AI and ML workloads across one or more cloud platforms or third-party technologies such as Google PaLM and Vertex AI, Azure OpenAI Services and Azure ML, AWS Bedrock and SageMaker, Dataiku, Databricks, Snowflake Snowpark and Snowflake Cortex, and DataRobot.
Experience architecting scalable, high-performance, and cost-optimised AI and ML solutions using serverless technologies, containerised deployments, Kubernetes, and GPU-based compute infrastructure.
Working knowledge of generative AI and hands-on experience deploying and hosting large foundation models.
Experience with large language model architectures such as transformers, GANs, and VAEs, along with fine-tuning techniques, retrieval-augmented generation, contextual embeddings, vector databases, and semantic search tools
Experience using deep learning frameworks and tools such as PyTorch, TensorFlow, and LangChain.
Experience designing and implementing MLOps frameworks that support robust model training, testing, monitoring, and continuous improvement life cycles.
Experience developing and integrating APIs, particularly for serving machine learning models.
Experience building enterprise-integrated solutions that deliver end-to-end capabilities and the ability to recommend appropriate architectural approaches based on use case requirements.
To apply for this role please submit your CV or contact Dillon Blackburn (see below)
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