AI & Data Science Team Lead / Architect
iForce Connect · Dublin, Ireland ·
- Seniority
- Lead
- Employment
- Contract
- Category
- Data science
- Experience
- 1+ years
iForce Connect · Dublin, Ireland ·
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Lead the design and deployment of agentic AI systems and enterprise copilots using AWS Bedrock, SageMaker, and Microsoft Copilot Studio, while mentoring a team of data scientists and AI engineers.
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.
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.
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.
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.
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.
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.
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.