Senior ML Engineer – Fintech

About Skillwork:

We are a London-born diverse group of techies, entrepreneurs, and business specialists with years of experience in developing innovative digital products for our clients, from startups to corporates. With prominent clients such as GSK, Emirates Development Bank,The European Commission, Tide Bank, Corpay and a variety of start-ups and scale-ups and partners such as UCL, NHS and Accenture, we find pride in our unique approach to solving our clients’ problems with technology.

At Skillwork, we firmly believe that genuine innovation thrives in a culture that is brimming with happy and talented individuals, and where clients engage not with a contractor, but with a true partner that they can talk to and rely on.

About the role:

Join our team as a Machine Learning Engineer and make a meaningful impact while working in a supportive and collaborative environment. You will be part of our FinT ech Client’s Data Centre for Excellence, working together with data scientists, engineers and analysts. This role is ideal for someone who creates value through innovation and teamwork.

You will be working closely with our cross-functional teams, and business leaders, to drive strategic decision-making across multiple regions, including the UK, Europe, Australia, and New Zealand. We believe in empowering you to grow your skills, and you will have the opportunity to leverage your machine learning expertise to solve key business challenges. We promote a healthy work-life balance and offer a culture where your contributions are recognized.

Responsibilities:

● Lead cross-functional teams including data scientists, data engineers and data analysts, as well as manage expectations of business stakeholders and work to understand businessrequirements and develop solutions that have an impact on the bottom line.

● Own the end-to-end development of AI & ML models for real-time and batch-based products,as well as running customer-facing services (APIs) in AWS cloud-based environments.

● Drive and advocate adoption of best practices in cloud-native MLOps (monitoring + alerting).

● Create and be responsible for data pipelines and automation processes that enhanceefficiency, accuracy and quality in data collection and data preparation.

● Mentor junior ML engineers to support their growth and encourage upskilling.

● Document analytical processes, methodologies, and findings to ensure clarity, reproducibility,

and knowledge sharing across teams.

Requirements:

● A Master degree in STEM (Computer Science, ML/AI, Physics, Engineering), along with 5+ years of experience delivering measurable value through data in a commercial setting.

● Expert in Python 3.12+ and latest versions of frameworks, including Scikit-learn, Numpy and Pandas with practical Data Science experience of Spark SQL and Jupyter notebooks.

● Experience with MLOps pipelines on AWS and building, training, testing, and deploying ML models at scale on cloud-native infrastructure.

● Experience with CI/CD tooling and AWS products like EC2, S3, CloudWatch and AWS CDK.

● Experience with monitoring and alerting solutions such as NewRelic, Datadog or PagerDuty.

● Strong software testing approach to ensure high test coverage for financial use cases.

● Strong understanding of the structure and suitability of different ML models and approaches for training trade-offs with experience in testing and accuracy for business performance.

● Well-versed in version control, with practical experience of Git+Gitflow on Github or Gitlab.

● Excellent communications skills, able to translate technical finding into business insights.

● Comfortable managing priorities in a fast-paced and dynamic work environment.

● A self-starter, detail-oriented who thrives on integrity, initiative, team-playing and results.

● Desire to stay up-to-date and experiment with the latest Gen AI research.

Preferred Qualifications (Nice-to-Have)

● PhD in STEM, plus 2+ years delivering value through data in a commercial environment.

● Experience in engineering leadership across multiple product roadmaps.

● Experience containerising ML models for AWS EKS Kubernetes and serverless serving.

● Experience with LangChain framework and prompt engineering on LLMs via LLMOps.

● Experience with T erraform and building resilient cloud infrastructure as code (IaC).

● Exposure to financial services and challenges with pricing and credit decision-making.

Benefits:

● 25 days of annual paid leave

● Additional health insurance via UNIQA

● MultiSport card

● Employee Referral Program

● Opportunities for professional growth and career development

● Dynamic and collaborative work environment

● Flexible work schedule and fully remote work