🚀 Machine Learning Engineer — Applied AI & Data Systems
Recruiting in 2026
Are you excited by data, algorithms, and building intelligent systems that actually do things?
This role is focused on turning raw data into deployed machine learning solutions used at scale.
You'll work across the full ML lifecycle — from data ingestion to model deployment — in a collaborative, engineering-driven environment.
🧠 What You'll Work On
This role is focused on turning raw data into deployed machine learning solutions used at scale.
You'll work across the full ML lifecycle — from data ingestion to model deployment — in a collaborative, engineering-driven environment.
🧠 What You'll Work On
- Designing, training, and deploying machine learning models
- Working with structured & unstructured datasets at scale
- Building data pipelines and model-serving infrastructure
- Improving model accuracy, robustness, and explainability
- Collaborating with product and engineering teams to ship real features
- 🔹 Develop ML models for prediction, classification, and ranking
- 🔹 Create feature pipelines using SQL + Python
- 🔹 Evaluate model performance and iterate using real-world feedback
- 🔹 Deploy models into production systems
- 🔹 Monitor drift, accuracy, and performance over time
- 🔹 Document assumptions, experiments, and decisions
- Strong experience with:
- Python 🐍
- Data science libraries (NumPy, Pandas, SciPy)
- Machine learning frameworks (e.g. deep learning or classical ML tools)
- Solid understanding of:
- Supervised vs unsupervised learning
- Feature engineering
- Model evaluation metrics
- Overfitting & generalisation
- Experience working with:
- SQL databases
- Data warehouses
- REST or batch-based inference systems
- Experience with:
- Cloud-based ML platforms ☁️
- Containerisation (Docker-style workflows)
- Model monitoring & observability tools
- Knowledge of:
- NLP or computer vision
- Recommendation systems
- Time-series forecasting
- ✔️ Models are accurate, stable, and explainable
- ✔️ Data pipelines are reliable and scalable
- ✔️ Stakeholders understand how and why models behave
- ✔️ Experiments are reproducible and well-documented
- Agile, iterative development 🔄
- Strong focus on code quality and testing
- Open discussion and peer review culture
- Bias towards practical, deployable solutions
- Competitive salary based on experience 💶
- Flexible working hours ⏰
- Remote-first setup 🏡
- Learning budget for courses, books, and conferences 📚
- Full-time
- Permanent
Referral reward: $750
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