Machine Learning / Applied AI Engineer

At QuantSpark, pioneering the strategic application of data, analytics, and AI is at the heart of our work. We empower businesses and government to thrive, not just for profits, but for measurable human and societal benefit. This means tackling real-world challenges, innovating at scale, and forging solutions that leave a legacy of prosperity and efficiency.

Here, engineers and strategists collaborate daily, compressing years of traditional progression into an accelerated path.

As a Machine Learning / Applied AI Engineer, you will design, build, and deploy intelligent solutions that bridge advanced machine learning and real-world product needs.

In this role, you'll develop reproducible training pipelines, evaluate and serve models through APIs, integrate open-source or pre-trained AI models, and ensure our AI applications are reliable, observable, and scalable in production.

You won't be doing research-grade AI - you'll be turning proven models and techniques into real, shippable applications.

Our Client Portfolio

We Work With High-growth Organisations And Sector Leaders

• Financial Services & Private Equity: Delivering AI-powered analytics, risk, and operational tools.
• Retail & Consumer Brands: Optimising pricing, inventory, and engagement through actionable analytics.
• Asset Management: Developing investment infrastructure and automated risk management systems.
• Public Sector: Trusted by government departments for fraud analytics, predictive maintenance, and operations optimisation.
• SaaS & Manufacturing: Driving technological transformation and efficiency across verticals., * Develop and Train ML Models: Design lightweight but production-minded machine learning pipelines for classification, regression, or prediction use cases.
• Operationalise Models: Package and deploy models as REST APIs or microservices (FastAPI, Flask, or equivalent).
• Leverage Applied AI & Foundation Models: Integrate and fine-tune pretrained models (e.g., from Hugging Face or similar libraries) for text, tabular, or simple image tasks.
• Ensure Reproducibility & Quality: Track experiments, manage artifacts (models, metrics, parameters), and use ML best practices for versioning and reproducible results.
• Collaborate Across Disciplines: Work closely with data engineers and software developers to build scalable, maintainable end-to-end ML applications.
• Monitor and Improve Deployed Models: Collect performance metrics, identify drift, and support model lifecycle operations in partnership with the MLOps team.

• You have 2-5 years of experience in ML Engineering, Data Science, or Applied AI
• Bachelor's or higher in Computer Science, Data Science, or a related field
• You have strong programming skills in Python 3.8+
• You're experienced in building and training models using scikit-learn or similar ML frameworks
• You've experience serving models via FastAPI / Flask or another web API tool
• You have an understanding of model evaluation metrics and validation best practices
• You have familiarity with typical ML lifecycle management (training * evaluation * inference * retraining)
• You have hands on experience developing on CPU/GPU environments (local or cloud)

• Values cutting-edge AI innovation and invests in scalable, production-grade machine learning systems.
• Encourages cross-disciplinary collaboration and continuous learning.
• Supports best practices in ML engineering and fosters a culture of performance optimisation and technology adoption.

I'm looking to work with...

• Talented data scientists, software engineers, product managers, and stakeholders across disciplines who are passionate about AI.
• Teams that value clear communication and collaborative problem-solving to deliver impactful AI solutions.

Equal Opportunities