I'm currently working with an innovative company at the forefront of the AgriTech sector, leveraging AI and machine learning to transform the future of farming and sustainable food production. They are looking to hire a Machine Learning Engineer to help build intelligent solutions that improve crop yields, optimise resource usage, and drive greater efficiency across agricultural operations.
This is an opportunity to apply cutting-edge machine learning techniques to real-world challenges, helping shape a more sustainable and data-driven farming industry.
The Role
As a Machine Learning Engineer, you'll play a key role in developing and deploying production-grade ML solutions that support precision agriculture and smart farming initiatives. You'll work alongside software engineers, data scientists, and domain experts to deliver scalable systems capable of processing large volumes of sensor, satellite, and operational data.
Responsibilities
- Design, build, and deploy machine learning models into production environments.
- Develop data pipelines capable of processing large-scale agricultural and environmental datasets.
- Build predictive models to improve crop health, yield forecasting, irrigation optimisation, and resource management.
- Work with structured and unstructured data from IoT devices, sensors, drones, and satellite imagery.
- Collaborate with engineering and product teams to translate business challenges into AI-driven solutions.
- Implement MLOps best practices, including model monitoring, retraining, and automated deployment.
- Explore emerging technologies including Generative AI and advanced analytics to enhance farming operations.
What They're Looking For
Essential Experience
- 3+ years of commercial experience building machine learning solutions.
- Strong Python development skills.
- Experience with PyTorch, TensorFlow, Scikit-learn, or similar ML frameworks.
- Strong understanding of feature engineering, model evaluation, and optimisation techniques.
- Experience working with large datasets and SQL databases.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Experience with Docker, Kubernetes, and CI/CD pipelines.
- Excellent communication skills and the ability to work within cross-functional teams.
Desirable
- Experience working with geospatial data, satellite imagery, computer vision, or IoT datasets.
- Exposure to time-series forecasting and predictive analytics.
- Experience with Generative AI, LLMs, or MLOps platforms such as MLflow or Kubeflow.
- Knowledge of distributed computing frameworks such as Spark or Ray.
Why Join?
Agriculture is undergoing a technological revolution, and my client is leading that change. Their mission is to empower farmers with intelligent tools that increase productivity, reduce waste, and support more sustainable food production.
This is an opportunity to work on meaningful problems where your models will have a tangible impact—not only on business performance but on the future of global agriculture.
If you're a Machine Learning Engineer looking to apply AI to one of the world's most essential industries, I'd be keen to speak with you.
Sign in
To continue your applicationor continue with email
By continuing you agree to our Terms & Privacy Policy.
