Senior Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in India. The Senior Machine Learning Engineer will help build and shape a new machine learning function within a remote-first payments technology environment. Working closely with engineering leadership and an experienced Staff Engineer, you will establish the foundations for applying machine learning to real-world payment challenges. Your primary focus will be developing intelligent payment routing solutions that improve transaction performance and deliver measurable business outcomes. You will own the complete machine learning lifecycle, from researching and testing approaches to deploying, monitoring, and optimizing models in production. Collaborating with product, data, engineering, and customers, you will identify valuable use cases and turn experimentation into reliable, scalable features. This is an opportunity to influence technical direction, establish best practices, and make meaningful contributions in a growing organization where initiative and independent problem-solving are essential.

As a Senior Machine Learning Engineer, you will take ownership of machine learning initiatives from initial exploration through production deployment and continuous improvement. You will work across teams to identify opportunities, evaluate solutions, and establish reliable practices that make machine learning an effective part of the payment infrastructure. End-to-End Machine Learning Development: Own the full lifecycle of machine learning features, including research, experimentation, model training, deployment, monitoring, and ongoing optimization. Intelligent Payment Routing: Develop and productionize machine learning models that improve payment routing decisions, helping optimize transaction performance and payment outcomes. ML Infrastructure and MLOps: Build and improve the infrastructure required to operate models reliably, including model versioning, CI/CD pipelines, observability, and production monitoring. Experimentation and Evaluation: Design and run experiments, including A/B tests, to evaluate model performance and determine whether proposed solutions deliver measurable improvements. Cross-Functional Collaboration: Partner with product, data, and engineering teams to identify valuable machine learning use cases and translate promising ideas into shipped, measurable features. Customer Engagement: Speak directly with customers to validate assumptions, understand real-world requirements, and assess how machine learning solutions perform in practice. Technical Direction: Help define priorities, evaluate technical trade-offs, and determine which machine learning opportunities offer the greatest potential impact. Engineering Mentorship: Support and mentor engineers adopting machine learning techniques, sharing knowledge about experimentation, model evaluation, and practical implementation decisions. Standards and Best Practices: Establish repeatable patterns and engineering practices that improve the reliability, maintainability, and scalability of machine learning solutions. Continuous Improvement: Take a pragmatic, impact-driven approach to model selection and optimization, prioritizing effective solutions over unnecessary complexity. Requirements: The ideal candidate has senior-level experience in machine learning engineering, data science, or applied research, with a proven ability to take models beyond experimentation and into production. You should combine strong Python and statistical skills with sound software engineering judgment, cloud experience, and the independence needed to operate in an evolving technical environment. Professional Experience: Senior-level experience in machine learning engineering, data science, or applied research, including successful production deployments through APIs, batch processing, or streaming systems. Python and ML Frameworks: Strong Python programming skills and hands-on experience with machine learning libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, or Keras. Statistical Thinking: Ability to design and run experiments, conduct A/B testing, interpret results, and communicate what the evidence does and does not demonstrate. Production Machine Learning: Experience taking models from research and experimentation through deployment, monitoring, and continuous improvement in production environments. Software Engineering and Infrastructure: Solid understanding of modern software engineering practices, infrastructure, data tooling, and the challenges associated with managing the full machine learning lifecycle. MLOps Knowledge: Understanding of the capabilities and operational requirements of reliable machine learning platforms, including deployment workflows, versioning, monitoring, and observability. Reinforcement Learning: Familiarity with reinforcement learning concepts, particularly multi-armed or contextual bandits, and an understanding of where these approaches can be applied. Production experience is not required. Cloud Platforms: Experience with cloud infrastructure, preferably AWS. Experience with Google Cloud Platform or Microsoft Azure is also suitable. Problem-Solving and Independence: Ability to research, develop, test, and productionize solutions independently while making informed technical decisions in ambiguous situations. Collaboration and Communication: Ability to work effectively with product, data, engineering, and business stakeholders, including engaging directly with customers to validate solutions. Preferred Domain Experience: Exposure to payments or e-commerce is an advantage but is not required, as domain knowledge can be developed on the job. Adaptability: Willingness to help shape a developing machine learning function, establish new practices, and work without a fully defined roadmap. Benefits: Fully remote work environment with a globally distributed team. Competitive share options. Flexible holiday policy, with a minimum of 25 days to be taken. Access to coworking spaces across major cities. Team workations and an annual company retreat. High-quality equipment suited to your role. £500 contribution toward setting up your home office. Generous learning budget to support continued professional development. Private medical insurance. Additional perks and benefits depending on location. Supportive onboarding and a collaborative working environment. Opportunities to shape a new machine learning function and influence technical direction. A culture that values inclusion, diversity, equal treatment, and different perspectives.