Senior Software Engineer (Platform, Orion)

Preqin
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Software & Data Engineer (Java/Python)

Senior AI Software Engineer - Gen AI & NLP

Senior Data Engineer - Advertising Tech

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

About the Orion Team 
The Orion project transforms the core of Preqin by changing the way we collect data. The team moves fast and independently, while also contributing to setting best practices across teams. We combine automation with Machine Learning and AI and with advanced data and engineering solutions. 
 
Job overview 
At Preqin data is at the heart of everything we do, we operate a world class data research team and provide alternative asset data highly prized by thousands of customers worldwide. Preqin engineering is evolving into a fast-paced and autonomous culture, our Software Engineers have an opportunity to significantly accelerate these changes and help shape our organisation for the future. 

Working in the platform team, you will be responsible for the technical excellence of our platform services, shaping, collaborating, and creating new and changing existing implementations as necessary to raise the bar technically and improve the way we manage our services. In platform we own some of the business-critical services such as authentication as well as a series of new work streams focused on user management, data management and architectural oversight. You will also work with technical teams across the business supporting them to build, and implementing yourself, a variety of technological solutions. 

The platform team is critical to the success of Preqin’s technology strategy providing the foundations for cross-team services and enablement for teams located in other business units. The team has recently adopted this direction and there is tremendous opportunity to impact the way we do technology at Preqin and helping to contribute to Preqin’s mission to unleash the power of data, increasing transparency in alternative assets and empowering the finance community to make better decisions across the global alternatives market. 

Department Engineering Employment Type Permanent - Full Time Location London Workplace type Hybrid Reporting To Engineering Manager - Engineering What you’ll be doing:

  • Accelerate data collection at scale from millions of sources. 
  • Design, build, and deploy workflows at scale that seamlessly combine AI/ML with human expertise. 
  • Elevate development standards and empower others adopt them through re-usable services, frameworks, templates, and knowledge sharing. 
  • Collaborate with engineering teams across the business to improve time to value and to ensure that the best options for internal technical solutions are known. 
  • Explore new technologies, approaches, and ideas that help to drive our business goals in unexpected ways. 

What you’ll bring to us:

  • Strong technical ability across the full stack:  
    • Backend: Python (FastAPI, pydantic) 
    • Frontend: REACT JS (webpack module federation, MFEs) 
    • Databases: postgres, Snowflake 
    • Cloud: AWS resources such as EC2, secrets manager, ECR, RDS, CloudWatch 
  • Knowledge and experience using infrastructure as code (terraform), CI/CD pipelines and container orchestration software such as Kubernetes with package managers such as HELM. 
  • A “let’s do it” and “challenge accepted” attitude when faced with the less known or challenging tasks. “Because we’ve always done it this way” is not a phrase you like to use. 
  • Ability to perform well in a fast-paced environment, developing iterative sustainable solutions with best practices (security, code quality, documentation) and long-term vision. 
  • Curiosity and willingness to learn about new technologies, ways of working and acquire new skills possessing a growth mindset. 
  • Understanding that generating positive outcomes requires knowledge of the stakeholder and the problem space to allow effective use of your technical knowledge ability. 
  • Passion to improve the capacity of engineering teams to deliver value through collaboration, excellent tooling, and thin configurable services. 
  • Excitement to collaborate with technical and non-technical colleagues across teams. 
  • Qualifications are not as essential as experience. If you feel you have work examples and projects that illustrate what we need, we’re happy to have a conversation. 

Why join us?

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.