About the Role: We are seeking a Machine Learning Engineer to help design and build scalable systems that enhance the performance and efficiency of our data-driven tools. This role will play a pivotal part in upgrading our current platform and developing new solutions that support the growing needs of the business. You’ll work closely with cross-functional teams, including data scientists, product engineers, and DevOps, to bring robust and scalable machine learning applications into production. Key Responsibilities: • Design and implement scalable systems and APIs to serve machine learning models in production. • Upgrade existing tools and infrastructure to improve performance, scalability, and reliability. • Collaborate with the broader Data Science team to scope tasks and drive project execution. • Develop and maintain data pipelines using PySpark. • Optimize web services with effective caching strategies and performance tuning. • Leverage Azure Cloud infrastructure for deployment and scaling of ML applications. • Participate in weekly syncs with the Data Science team and contribute to the long-term architecture roadmap. • Ensure systems are well-documented, reliable, and support seamless integration with other platforms. Must-Have Qualifications: • 3+ years of experience in machine learning engineering or a similar role. • Strong programming skills in Python and PySpark. • Experience building and integrating RESTful APIs for ML models. • Solid understanding of web services and caching strategies (e.g., Redis, CDN). • Proven experience working with cloud platforms, preferably Azure. • Demonstrated ability to build scalable, production-ready systems. Nice-to-Haves: • Experience with containerization tools (e.g., Docker, Kubernetes). • Familiarity with MLOps tools and practices. • Background in distributed systems or data architecture. • Experience in Agile/Scrum environments.