Machine Learning Engineer

Data Science Festival; Greater London; Hybrid; GBP 55,000 - 75,000; Full time

Benefits offered by this job

Competitive salary with annual reviews

Hybrid working model offering flexibility

Generous holiday allowance

Onsite wellness facilities

Access to wellbeing support services

Job summary

Data Science Festival is seeking a Machine Learning Engineer to design and build robust data pipelines while transforming ML prototypes into production-ready systems. This role directly influences how millions engage with products, ensuring data and AI strategies scale effectively.

You will work closely with analysts and data engineers in a hybrid setting, focusing on reliable, scalable models. Benefits include competitive salary, generous holidays, and career progression opportunities.

Qualifications

• Degree in a relevant field such as Computer Science, Engineering, or Mathematics.
• Strong knowledge of Python and SQL.
• Hands-on experience with cloud platforms (GCP or Azure) and Databricks.
• Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML.

Responsibilities

• Building and maintaining end-to-end data pipelines and feature engineering workflows.
• Deploying and monitoring ML models in production using various tools.
• Driving best practices in MLOps, including CI/CD and model governance.
• Supporting the data warehouse to ensure data quality and governance.
• Collaborating with cross-functional teams to deliver trusted datasets.

Skills

Python

SQL

Cloud platforms (GCP or Azure)

Databricks

MLflow

Vertex AI

CI/CD pipelines

Elasticsearch

Digital/web analytics

Spark

Education

Degree in Computer Science, Engineering, Mathematics, or a related field

Tools

Azure ML

Job description

We are currently looking for a Machine Learning Engineer to join our client’s data team. This is a hands‑on role where you’ll design and build robust data pipelines, transform ML prototypes into production‑ready systems, and champion MLOps best practices across the business. As a Machine Learning Engineer, you’ll play a crucial role in ensuring our clients’ data and AI strategy scales effectively, directly influencing the way millions of people engage with their products every day.

Opportunity

This is a unique chance to combine data engineering with machine learning in a high‑impact environment. You’ll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable, and production‑ready. Unlike many roles in the tech sector, this Machine Learning Engineer role gives you the visibility of seeing your work applied at scale, powering decision‑making and user experiences for a vast audience.

Day‑to‑day

Your day‑to‑day will include:

• Building and maintaining end‑to‑end data pipelines and feature engineering workflows.
• Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML.
• Driving best practices in MLOps, including CI/CD, experiment tracking, and model governance.
• Supporting the data warehouse and ensuring data quality, governance, and accessibility.
• Collaborating with cross‑functional teams to deliver trusted datasets and insights.

Benefits

• Competitive salary with annual reviews.
• Hybrid working model offering flexibility.
• Generous holiday allowance that increases with service.
• Onsite wellness facilities, subsidised meals, and gym access.
• Access to wellbeing support services and employee assistance programmes.
• Clear career progression and opportunities to work with cutting‑edge tech.

Skills and Experience

• Degree in Computer Science, Engineering, Mathematics, or a related field.
• Strong knowledge of Python and SQL.
• Hands‑on experience with cloud platforms (GCP or Azure) and Databricks.
• Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML.
• Nice‑to‑have: Experience with Spark, CI/CD pipelines, and orchestration tools.
• Nice‑to‑have: Knowledge of Elasticsearch or digital/web analytics platforms.
• Nice‑to‑have: Understanding of the full machine learning lifecycle, from experimentation to evaluation.