This is a contract position. Ireland & UK-based candidates (travel to Ireland).
Role Overview
As the AI & Data Science Team Lead you will spearhead the design development and deployment of agentic AI solutions and advanced machine learning systems that solve complex business and customer problems at scale.
This role combines deep technical leadership with AI architecture ownership. You will guide the adoption of cloud platforms such as Amazon Bedrock Bedrock Agents SageMaker and Microsoft Copilot Studio to deliver secure explainable and production-ready AI capabilities across the organisation.
Spanning strategy discovery architecture and delivery you will shape how AI agents copilots and decisioning systems are designed orchestrated deployed and governed while mentoring a high-performing team of data scientists and AI engineers.
You will be a senior member of the AI & Data Science team within the Enterprise Data & Analytics Division working at the intersection of Data Science AI Engineering Enterprise Architecture Product and Business Teams. You will play a key role in establishing AI platform standards reusable agent frameworks and responsible AI practices helping to embed AI-driven decision making across the organisation in a safe and scalable way.
Key Responsibilities
AI Leadership & Agentic Systems
Lead Agentic AI Design: Provide technical leadership on the design and implementation of agentic AI systems including multi-agent workflows tool-using agents orchestration patterns and human-in-the-loop designs.
Drive Enterprise Copilots: Lead the development of enterprise copilots and AI assistants using Microsoft Copilot Studio aligning them to real business workflows and measurable outcomes.
Establish Frameworks: Define best practices for prompt engineering memory strategies tool calling evaluation and agent reliability.
AI Architecture & Platform Design
Own End-to-End Architecture: Drive AI solution architecture from initial experimentation through to full production using AWS Bedrock SageMaker and supporting AWS services.
Collaborate on Infrastructure: Partner with cloud data and security architects to design secure scalable and compliant AI platforms including model lifecycle management and environment separation.
Standardise Assets: Drive the development of reusable AI assets agent templates and reference architectures to accelerate delivery across multiple teams.
Advanced Analytics & Engineering
Deploy Advanced Models: Lead the design and deployment of advanced machine learning and GenAI models (including forecasting NLP and decision optimisation) ensuring models are robust explainable and production-ready.
Govern Model Performance: Guide the team on model evaluation monitoring drift detection and continuous improvement within regulated environments.
Uphold Engineering Excellence: Ensure strong engineering practices across version control CI/CD testing and deployment of AI systems.
Discovery Delivery & Stakeholder Engagement
Identify High-Value Use Cases: Partner with business stakeholders product owners and analysts to identify AI use cases shape business cases and run structured discovery engagements.
Translate Business Needs: Convert ambiguous business challenges into clear AI solution designs with defined success metrics.
Influence & Educate: Communicate complex AI concepts clearly and persuasively to senior non-technical stakeholders.
Continuous Growth: Active participation in Community of Practice meetings training sessions and learning settings for professional development and career enhancement.
Candidate Requirements
Essential
Experience: 6 years of experience in data science machine learning or applied AI with at least 1 years leading technical teams or AI delivery.
Production Deployment: Strong proven experience designing and deploying end-to-end AI solutions in production environments.
Copilot Studio: Hands-on experience with Microsoft Copilot Studio and building AI assistants or copilots that integrate seamlessly with enterprise systems.
Cloud Platform Mastery: Experience working with AWS AI/ML services (e.g. Amazon SageMaker Amazon Bedrock) or equivalent AI platforms for experimentation evaluation and deployment.
Technical Stack: Solid background in Python (and/or R) for data science and machine learning.
AI Architecture: Strong understanding of AI architecture including model orchestration APIs cloud services and integration patterns.
Communication: Proven ability to communicate complex analytical and AI concepts effectively to both technical and business audiences.
Governance & Regulation: Experience working within regulated environments with solid knowledge of GDPR and AI governance expectations.
Desired
Experience designing or deploying agentic or multi-agent AI systems.
Prior experience within banking or the broader financial services industry.
Deep expertise in areas such as NLP GenAI deep learning or decision intelligence.
Practical experience with MLOps / LLMOps practices and tooling.
Cloud certifications (Azure or AWS) or deep practical cloud experience.
Experience building and deploying GenAI applications at scale.
This is a contract position.Ireland & UK-based candidates (travel to Ireland).Role OverviewAs the AI & Data Science Team Lead you will spearhead the design development and deployment of agentic AI solutions and advanced machine learning systems that solve complex business and customer problems at sc...
This is a contract position. Ireland & UK-based candidates (travel to Ireland).
Role Overview
As the AI & Data Science Team Lead you will spearhead the design development and deployment of agentic AI solutions and advanced machine learning systems that solve complex business and customer problems at scale.
This role combines deep technical leadership with AI architecture ownership. You will guide the adoption of cloud platforms such as Amazon Bedrock Bedrock Agents SageMaker and Microsoft Copilot Studio to deliver secure explainable and production-ready AI capabilities across the organisation.
Spanning strategy discovery architecture and delivery you will shape how AI agents copilots and decisioning systems are designed orchestrated deployed and governed while mentoring a high-performing team of data scientists and AI engineers.
You will be a senior member of the AI & Data Science team within the Enterprise Data & Analytics Division working at the intersection of Data Science AI Engineering Enterprise Architecture Product and Business Teams. You will play a key role in establishing AI platform standards reusable agent frameworks and responsible AI practices helping to embed AI-driven decision making across the organisation in a safe and scalable way.
Key Responsibilities
AI Leadership & Agentic Systems
Lead Agentic AI Design: Provide technical leadership on the design and implementation of agentic AI systems including multi-agent workflows tool-using agents orchestration patterns and human-in-the-loop designs.
Drive Enterprise Copilots: Lead the development of enterprise copilots and AI assistants using Microsoft Copilot Studio aligning them to real business workflows and measurable outcomes.
Establish Frameworks: Define best practices for prompt engineering memory strategies tool calling evaluation and agent reliability.
AI Architecture & Platform Design
Own End-to-End Architecture: Drive AI solution architecture from initial experimentation through to full production using AWS Bedrock SageMaker and supporting AWS services.
Collaborate on Infrastructure: Partner with cloud data and security architects to design secure scalable and compliant AI platforms including model lifecycle management and environment separation.
Standardise Assets: Drive the development of reusable AI assets agent templates and reference architectures to accelerate delivery across multiple teams.
Advanced Analytics & Engineering
Deploy Advanced Models: Lead the design and deployment of advanced machine learning and GenAI models (including forecasting NLP and decision optimisation) ensuring models are robust explainable and production-ready.
Govern Model Performance: Guide the team on model evaluation monitoring drift detection and continuous improvement within regulated environments.
Uphold Engineering Excellence: Ensure strong engineering practices across version control CI/CD testing and deployment of AI systems.
Discovery Delivery & Stakeholder Engagement
Identify High-Value Use Cases: Partner with business stakeholders product owners and analysts to identify AI use cases shape business cases and run structured discovery engagements.
Translate Business Needs: Convert ambiguous business challenges into clear AI solution designs with defined success metrics.
Influence & Educate: Communicate complex AI concepts clearly and persuasively to senior non-technical stakeholders.
Continuous Growth: Active participation in Community of Practice meetings training sessions and learning settings for professional development and career enhancement.
Candidate Requirements
Essential
Experience: 6 years of experience in data science machine learning or applied AI with at least 1 years leading technical teams or AI delivery.
Production Deployment: Strong proven experience designing and deploying end-to-end AI solutions in production environments.
Copilot Studio: Hands-on experience with Microsoft Copilot Studio and building AI assistants or copilots that integrate seamlessly with enterprise systems.
Cloud Platform Mastery: Experience working with AWS AI/ML services (e.g. Amazon SageMaker Amazon Bedrock) or equivalent AI platforms for experimentation evaluation and deployment.
Technical Stack: Solid background in Python (and/or R) for data science and machine learning.
AI Architecture: Strong understanding of AI architecture including model orchestration APIs cloud services and integration patterns.
Communication: Proven ability to communicate complex analytical and AI concepts effectively to both technical and business audiences.
Governance & Regulation: Experience working within regulated environments with solid knowledge of GDPR and AI governance expectations.
Desired
Experience designing or deploying agentic or multi-agent AI systems.
Prior experience within banking or the broader financial services industry.
Deep expertise in areas such as NLP GenAI deep learning or decision intelligence.
Practical experience with MLOps / LLMOps practices and tooling.
Cloud certifications (Azure or AWS) or deep practical cloud experience.
Experience building and deploying GenAI applications at scale.
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