AI Lead / Data & AI Enablement Lead

We are looking for an experienced AI Lead / Data and Artificial Intelligence Enablement Lead to identify high-value artificial intelligence opportunities, recommend suitable solution approaches, and enable data and engineering teams to adopt artificial intelligence capabilities.

The role involves working across generative artificial intelligence, retrieval-augmented generation, artificial intelligence agents, traditional machine learning, predictive modelling, and intelligent automation. The candidate will be responsible for converting business opportunities into secure, scalable, measurable, and production-ready solutions.

Major Responsibilities

• Partner with business, data, and technology teams to identify and prioritise artificial intelligence use cases.
  

• Evaluate whether rules, analytics, traditional machine learning, retrieval-augmented generation, generative artificial intelligence, or automation is the most suitable solution.
  

• Define artificial intelligence architectures, technology selections, delivery approaches, and implementation roadmaps.
  

• Lead the design and delivery of retrieval-augmented generation solutions, including document processing, chunking, embeddings, vector search, retrieval, re-ranking, and grounded response generation.
  

• Guide predictive modelling initiatives involving classification, regression, forecasting, anomaly detection, recommendations, and optimization.
  

• Assess managed models, open-source models, fine-tuning approaches, and custom modelling solutions.
  

• Develop secure integrations between artificial intelligence services, enterprise data platforms, applications, and business workflows.
  

• Establish evaluation frameworks to measure model quality, retrieval relevance, hallucination, accuracy, latency, cost, and business value.
  

Requirements

• Significant experience delivering artificial intelligence, machine learning, or advanced analytics solutions in production environments.
  

• Strong understanding of the complete artificial intelligence lifecycle, from problem definition and data preparation to evaluation, deployment, and monitoring.
  

• Practical experience implementing retrieval-augmented generation solutions.
  

• Strong knowledge of large language models, embeddings, vector databases, retrieval, re-ranking, prompt engineering, and model evaluation.
  

• Strong programming skills in Python and SQL.
  

• Experience with common data science and machine learning libraries.
  

• Knowledge of modern cloud data platforms such as Snowflake and Databricks.
  

• Experience developing structured, semi-structured, and unstructured data pipelines.
  

• Experience integrating artificial intelligence solutions through application programming interfaces, applications, and enterprise workflows.
  

• Knowledge of machine learning operations, large language model operations, continuous integration and continuous delivery, experiment tracking, model registries, and production monitoring.
  

Benefits

• Professional growth and career development in artificial intelligence and machine learning.
  

• Opportunities to work on generative artificial intelligence, predictive modeling, and intelligent automation solutions.
  

• Exposure to artificial intelligence architecture, experimentation, and production implementation.
  

• Collaboration with business, data engineering, and technology teams.
  

• Opportunity to develop reusable artificial intelligence architectures, frameworks, and engineering patterns.
  

• Experience in responsible artificial intelligence, governance, security, and regulatory compliance.
  

• Opportunities to coach teams and build organizational artificial intelligence capabilities.
  

• Exposure to modern cloud data platforms and enterprise artificial intelligence technologies.