What you'll do:

• ML System Development: Design, develop, and maintain scalable and efficient machine learning systems, including writing ML services and APIs.
• Model Deployment: Implement and manage the deployment of machine learning models into production environments, ensuring reliability and scalability.
• Infrastructure Management: Collaborate with infrastructure teams to optimize and manage the underlying systems supporting machine learning workflows.
• Data Pipeline Creation: Create robust and efficient data pipelines for collecting, processing, and preparing datasets for machine learning models.
• Collaboration: Work closely with data scientists, researchers, and cross-functional teams to integrate ML solutions into existing software infrastructure.
• Performance Optimization: Continuously optimize and improve the performance of machine learning algorithms and systems.
• Documentation: Develop and maintain documentation for machine learning systems, APIs, and data pipelines to ensure clarity and ease of use for team members.
• Partner with product owners in designing software that becomes part of researchers’ lives
• Arrive at technology decisions after extensive debates with other engineers
• Manage large projects from conceptualization, all the way through deployments
• Evolve an ecosystem of tools and libraries that make it possible for SciSpace to provide reliable, always-on, performant services to our users
• Partner with other engineers in developing an architecture that is resilient to changes in product requirements and usage
• Work on the user-interface side and deliver a snappy, enjoyable experience to your users

What makes you a great fit:

• 5+ years of experience including working on designing multi-component systems
• Strong grasp of one high-level language like Python.
• General awareness of SQL and database design concepts
• Solid understanding of testing fundamentals
• Strong communication skills
• Should have prior experience in managing and executing technology products.
• Prior experience working with high-volume, always-available web-applications
• Experience working with Next.js
• Experience in ElasticSearch
• Experience in Distributed systems
• Experience working with Kafka
• Experience working with graph databases
• Experience working with Start-up is a plus point.