Machine Learning Engineer

We are looking for a talented Software Engineer with a strong interest in machine learning to join our Data Discovery team. In this role, you will support the development and continuous improvement of machine learning models and retraining systems.

You will work with large datasets to build, train, and optimize models that drive automation. To deliver the best possible results, you’ll be expected to carefully process and prepare data, evaluate different algorithms, and select the most suitable one for each specific use case.

This is a great opportunity for someone passionate about turning data into intelligent solutions and eager to grow in the field of machine learning.

Requirements:

Bachelor’s in CS, Data Science, AI, or equivalent; proven ML/DL production experience.

Expert in Python (NumPy, pandas, scikit-learn) and SQL; hands-on with PyTorch or TensorFlow; familiarity with Hugging Face.

Prompt engineering, RAG, vector search, and model monitoring; fine-tuning is an advantage; strong feature engineering and classical ML.

Docker, AWS (ECS/Fargate, S3, Lambda, SageMaker); CI/CD (Jenkins); MLflow or Metaflow; Prefect or Step Functions; MySQL and DynamoDB.

Strong problem-solving skills and data driven decision-making

Vigilant about data quality, edge cases, and performance bottlenecks

Open, constructive collaboration with cross functional teams; mentorship of junior engineers when required

Clear articulation of complex technical concepts to technical and nontechnical stakeholders

Very good command of English, written and spoken

Key Responsibilities:

Own the full model lifecycle: problem scoping, data exploration, feature engineering, algorithm selection, training, evaluation, and iterative improvement

Containerise and serve models via FastAPI based microservices; ensure low latency inference, monitoring, and automated rollback strategies

Build reproducible pipelines (training, validation, inference) with CI/CD and infrastructure as code practices

Work closely with data engineers, product managers, and domain experts to align ML solutions with business goals

Champion clean code, unit/integration testing, code reviews, and documentation for long-term maintainability

Stay up to date with the latest ML/AI advancements; prototype and benchmark new techniques (e.g., LLM fine-tuning, vector search, on-device ML).

Diagnose and resolve production issues, optimise performance, and continually improve model robustness

What we offer:

Interesting and fulfilling projects

Great working environment in an international company

Open and friendly working atmosphere

Work-life balance

Hybrid working model of 2 days in the office and 3 days from home

4 weeks a year you can work from any location that you choose.