About You
We are looking for a passionate and experienced Staff Machine Learning Engineer to lead and scale our machine learning efforts at Razorpay. You will be instrumental in developing and deploying machine learning models that power our financial products and services, driving innovation and enhancing user experiences. As a senior IC, you will work closely with cross-functional teams to build robust and scalable machine learning systems and mentor junior engineers.
Responsibilities
- Architect and Scale ML Systems: Design, build, and scale machine learning systems that power Razorpay's financial products, focusing on fraud detection, credit risk assessment, and personalized financial recommendations, routing engines etc.
- End-to-End Project Ownership: Lead ML projects from conception to deployment, including data collection, preprocessing, feature engineering, modeling, evaluation, and monitoring.
- Collaboration and Leadership: Work closely with data scientists, ML engineers, product managers, and other stakeholders to develop and improve core components, infrastructure, and architecture. Drive strategic planning and execution for the ML roadmap.
- Mentorship and Development: Mentor and provide technical guidance to junior ML engineers, fostering a culture of learning, innovation, and continuous improvement.
- Stay Ahead of Trends: Stay abreast of the latest advancements in machine learning, data science, and related fields, and integrate new techniques and tools into our ML ecosystem.
- Technical Excellence: Ensure high-quality standards in the development, deployment, and maintenance of ML models, adhering to industry best practices.
Requirements
- Experience: 9+ years of industry experience, with at least 4 years in senior+ IC roles. Proven experience in leading and delivering large-scale ML projects.
- Technical Expertise: Deep practical knowledge of machine learning algorithms, statistical analysis, and advanced predictive modeling techniques. Proficiency in Python and experience with ML frameworks such as TensorFlow or PyTorch.
- Big Data and Cloud: Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure). Proficiency in data manipulation libraries (e.g., pandas, dask) and visualization tools (e.g., matplotlib, seaborn).
- Distributed Systems: Knowledge of large-scale distributed application architecture, design, implementation, and performance tuning.
- ML Lifecycle Management: Strong understanding of the machine learning lifecycle, from data collection to model deployment and monitoring. Experience with CI/CD tools (e.g., GitHub Actions, Jenkins).
- Educational Background: Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, Mathematics, or a related field, or equivalent industry experience.
- Communication Skills: Excellent written and verbal communication skills, with the ability to present complex ideas to diverse audiences and influence decision-making through data-driven insights.
- Problem-Solving: Exceptional problem-solving skills and strategic thinking, with a focus on delivering impactful ML solutions.
Preferred Qualifications
- Recommender Systems: Experience in building and optimizing large-scale recommender systems or ranking/retrieval systems.
- Publications: Relevant publications in top-tier applied machine learning conferences.
- Innovation: Demonstrated ability to innovate and drive the development of cutting-edge ML solutions that improve user satisfaction and business outcomes.
- Gen AI: Any experience in building pipelines with generative AI models at scale is a plus. Knowledge of LangChain, LlamaIndex, Dspy, agents, RAG etc. is highly valued.
Join us at Razorpay and be a part of a team that is transforming the financial landscape with data-driven insights and cutting-edge machine learning solutions.