Applied Machine Learning Developer, ML Specialist, AI Engineer

Design data pipelines, select algorithms, tune hyper-parameters, monitor model drift, and automate retraining. They work mainly with Python, cloud platforms, and modern MLOps frameworks in agile, cross-functional environments.

Technology start-ups scaling AI products|Large consultancies offering analytics solutions|Financial services pursuing risk modelling|Healthcare providers implementing diagnostic tools|Retail and e-commerce personalisation teams|Manufacturing firms deploying predictive maintenance

Start in a junior data or software role, build a portfolio of notebooks and small prototypes, contribute to open-source projects, then move into a dedicated ML position. Within five years, you could lead model-centric squads, mentor interns, and own key components of a firm’s AI stack. Continuous learning through conferences, online challenges, and community engagement accelerates progression.

Demand for machine learning talent continues to outstrip supply, driven by widespread adoption of predictive analytics and automation initiatives. Engineers who develop robust engineering practices around reproducibility, monitoring, and responsible AI are especially sought after. Advancement can lead to MLOps Lead, Principal Engineer, or technical fellow roles.

Python and scientific computing libraries mastery|TensorFlow or PyTorch deep learning workflows|Cloud services for scalable training pipelines|Docker and Kubernetes for reproducible deployments|Feature engineering and data preprocessing techniques|Automated testing and continuous integration for models|Model monitoring and drift detection tools

Clear technical communication with varied audiences|Problem-solving under time and data constraints|Collaborative agile teamwork and code reviews|Adaptability to new research and frameworks|Ethical judgement in model design choices|Mentoring juniors and sharing best practices