Senior ML Engineer

PEI Group is a subscriber-focused business intelligence company focusing on private investment markets in real estate, infrastructure, private equity, private debt, and specialist sector-specific activities within private asset classes. We provide industry-leading journalism, data, and market insight to subscribing clients via a wide portfolio of specialist brands supported by our robust and scalable digital publishing, analytics, and database platform.

Since its inception in 2001, we have grown into a subscriber-focused business intelligence company with our multi-talented global team of over 400 people spread across EMEA, USA & Asia, and our purpose is to inform and connect investment professionals across global, specialised markets.

As an ML Engineer, you will enhance PEI's growth and achievement by improving the customer experience with intelligent, customised solutions. You will leverage the latest AI and ML technologies to ensure our customer offerings are enjoyable, seamless, and at the cutting edge of industry innovation. In this role, you will work on cutting-edge ML/AI projects from ideation to production, emphasising maximising value delivery at a sustainable pace.

Roles and responsibilities:

• Implement and deploy machine learning models that drive measurable impact for our business 

• Partner with stakeholders to propose innovative solutions to existing business problems 

• Own the entire end-to-end machine learning delivery lifecycle, including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance 

• Create the tools, frameworks and libraries that enable the acceleration of our ML product delivery and improve our workflows 

• Take an active part in our AI and ML community and foster a culture of rigorous learning and experimentation.
   

Key Requirements:

• A minimum of 5+ years of experience as an ML Engineer is must.

• Experience in ML model development applying Data Science algorithms and GenAI

• Are proficient with Python and Databricks, including open-source data libraries (e.g. Pandas, Numpy, Scikit learn, etc.). 

• Strong understanding and experience of DevOps technologies such as Docker/Terraform and ML Ops practices and platforms/ frameworks like ML Flow ( Amazon Sagemaker/Azure Machine Learning).

• Strong experience in production using machine learning models.

• Working experience in any one of predictive modelling, optimisation or recommendation systems.

• Hands-on experience with agile delivery methodologies and CI/CD.

• Hands-on experience with Unit Testing.

• Have a broad understanding of data extraction, data manipulation and feature engineering techniques.

• Are familiar with statistical and mathematical methodologies.

• Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline.

• Excellent communication skills.

Good to Haves:

• PySpark/Ray for processing big data.

• DBT for data management.

• Understanding of NLP algorithms and techniques.

• Applied experience using graphs.

• Experience with Large Language Models (fine-tuning, RAG, agents, LangChain).

• Experience with cloud infrastructure.

• Good to have a masters degree in ML Engineering