AI Practice Lead - Generative AI & Data Science

About the role

Kagool, a fast-growing IT consultancy specialising in transforming complex global enterprises, is looking for an AI Practice Lead to head its Artificial Intelligence practice covering Africa, the UAE and global projects. The role covers pre-sales, solution architecture, project delivery and proof-of-concept activities across Generative AI, Machine Learning and Advanced Analytics, combining deep technical expertise with commercial and leadership skills.

Key responsibilities

• Lead the design and implementation of Generative AI and Data Science solutions using LLMs, machine learning models and multimodal systems.
• Engage with customers to identify opportunities, define use cases and demonstrate value through AI solutions and POCs.
• Support sales and pre-sales teams with proposals, estimates and solution blueprints.
• Architect and oversee delivery of AI and ML projects on Azure AI, Azure OpenAI, Databricks, Hugging Face, LangChain and RAG frameworks.
• Lead R&D to evaluate new AI technologies and turn them into enterprise-ready offerings.
• Guide AI workflow design and deployment, working with data scientists, ML engineers and product teams on applications such as chatbots, summarization, image synthesis and forecasting.
• Collaborate with DevOps/MLOps teams on deployment, monitoring and governance of models in production.
• Mentor junior engineers and data scientists, and represent Kagool in client meetings and industry events.

Required profile

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML or a related field.
• 10+ years of experience in AI/ML or Data Science, with at least 3 years in a leadership or practice management role.
• Strong experience in customer engagement, pre-sales and solution delivery.
• Excellent communication, leadership and stakeholder management skills.

Required skills

• LLMs such as GPT, Claude, Gemini or Mistral, and hands-on knowledge of LangChain, Hugging Face, OpenAI API and Azure OpenAI.
• RAG pipelines, embeddings, vector databases (Pinecone, FAISS, Chroma, Weaviate) and prompt engineering.
• Classical and deep learning (regression, classification, clustering, NLP, time-series), with Scikit-learn, TensorFlow and PyTorch.
• Azure Machine Learning, Databricks, Synapse, Azure DevOps, Docker, Kubernetes and CI/CD pipelines.
• Preferred: reusable AI assets, AI orchestration platforms, data privacy and compliance, open-source contributions.