Machine Learning Engineer

Machine Learning Engineer – Contract (12 Months) Location:

Remote (India) Contract Length:

12 months

We are looking for an experienced

Machine Learning Engineer

to join a global data and analytics programme, supporting the development of predictive automation solutions using large-scale commercial datasets. This role focuses on building, deploying, and optimising machine learning models on an enterprise data platform, with a strong emphasis on

PySpark

and distributed data processing.

This is a hands-on engineering role suited to someone who enjoys working with complex, multi-source data and translating advanced analytics into real-world commercial impact.

What you’ll be doing: Designing and implementing machine learning and deep learning models using

PySpark

on an enterprise data platform. Solving complex analytical problems using large, distributed commercial datasets. Building and maintaining end-to-end ML pipelines, from data ingestion through to deployment and monitoring. Collaborating closely with commercial, analytics, and data engineering stakeholders to align technical solutions with business goals. Identifying data drift, bias, and performance issues, and ensuring models generalise effectively in production. Delivering actionable outputs such as segmentation, targeting, and optimisation recommendations. Keeping up to date with advances in machine learning, big data, and automation technologies.

Key responsibilities: Ingesting, cleaning, and transforming large-scale datasets using

PySpark . Developing, evaluating, documenting, and monitoring ML models in a production environment. Designing and optimising ETL and data pipelines to support scalable ML operations. Working with stakeholders to define requirements and translate business objectives into technical solutions. Customising or extending ML libraries (e.g. PySpark MLlib)to maximise analytical value.

Skills & experience required: Strong experience working with large-scale, distributed data sets. Advanced knowledge of statistics, probability, algorithms, and machine learning concepts. Hands-on experience with

PySpark

and big data processing. Strong Python development skills. Experience with enterprise data platforms and data modelling. Excellent communication and collaboration skills. Proactive mindset with a willingness to learn new tool sand technologies.

Desirable experience: Background in machine learning within pharmaceutical, life sciences, or commercial analytics environments. Practical experience deploying ML solutions on enterprise data platforms. Exposure to additional ML frameworks such as

PyTorch

or

Keras

(nice to have). Bachelor’s or higher degree in computer science, mathematics, engineering, or a related quantitative field.