Senior Python Developer, AI and Machine Learning

We are looking for a senior Python developer with real depth in data science and machine learning, and the confidence to lead both a team and a client conversation. You will take business problems that arrive vague and turn them into models that ship, then keep them running well in production.
This is a hands-on role with ownership attached. You will design and deploy models, guide a small team through delivery, and explain what the results mean to people who do not think in confusion matrices.
Key responsibilities

• Lead the design, development and deployment of AI/ML models against real business problems
• Apply statistical modelling, machine learning, deep learning and NLP as the problem demands
• Build and maintain scalable data pipelines and model APIs, integrated into Python and Django applications
• Own the project lifecycle end to end: data discovery, feature engineering, model evaluation and productionisation
• Translate business requirements into analytical solutions and present findings to stakeholders and clients
• Lead client interactions, requirement discussions and solution presentations
• Mentor junior data scientists and engineers, and review deliverables for quality and consistency
• Work with cross-functional teams to identify and prioritise data science initiatives
• Ensure quality control, performance tuning and compliance across deployed models and services
• Track developments in AI/ML and bring the ones worth having into the team.

Desired Candidate Profile

• Strong Python across data analysis, machine learning and back-end development
• Proven experience developing and deploying ML models, not just prototyping them
• Django experience, including integrating ML models with web applications
• Command of libraries such as scikit-learn, TensorFlow, PyTorch and Keras
• Expertise in data wrangling, statistical analysis, feature engineering and model tuning
• REST APIs, SQL and NoSQL databases, and at least one cloud platform (AWS, GCP or Azure)
• Git, Docker and CI/CD pipelines
• Track record of client communication, requirement gathering and delivery
• Excellent written and verbal communication, including the ability to explain technical concepts to non-technical audiences

Nice to have

• Working knowledge of LLMs and their practical applications
• Big data tools such as Spark, Hadoop or Kafka
• Exposure to MLOps practices and tooling.