Responsibilities:

• Contribute to the development of software and solutions, emphasizing ML/NLP as a key component, to productize research goals and deployable services.
• Collaborate closely with the frontend team and research team to integrate machine learning models into deployable services.
• Utilize and develop state-of-the-art algorithms and models for NLP/ML, ensuring they align with the product and research objectives.
• Perform thorough analysis to improve existing models, ensuring their efficiency and effectiveness in real-world applications.
• Engage in data engineering tasks to clean, validate, and preprocess data for uniformity and accuracy, supporting the development of robust ML models.
• Stay abreast of new developments in research and engineering in NLP and related fields, incorporating relevant advancements into the product development process.
• Actively participate in agile development methodologies within dynamic research and engineering teams, adapting to evolving project requirements.
• Collaborate effectively within cross-functional teams, fostering open communication and cooperation between research, development, and frontend teams.
• Actively contribute to building an open, transparent, and collaborative engineering culture within the organization.
• Demonstrate strong software engineering skills to ensure the reliability, scalability, and maintainability of deployable ML services.
• Take ownership of the end-to-end deployment process, including the deployment of ML models to production environments.
• Work on continuous improvement of deployment processes and contribute to building a seamless pipeline for deploying and monitoring ML models in real-world applications

Qualifications:

• Degree in Computer Science or related discipline or equivalent practical experience, with a strong emphasis on machine learning and natural language processing.
• Proven experience and in-depth knowledge of ML techniques, with a focus on implementing deep-learning approaches for NLP tasks in the context of productizing research goals.
• Ability to apply engineering best practices to make architectural and design decisions aligned with functionalities, user experience, performance, reliability, and scalability in the development of deployable ML services.
• Substantial experience in software development using Python, Java, and/or C or C++, with a particular emphasis on integrating machine learning models into production-ready software solutions.
• Demonstrated problem-solving skills, showcasing the ability to address complex situations effectively, especially in the context of improving models, data engineering, and deployment processes.
• Strong interpersonal and communication skills, essential for effective collaboration within cross-functional teams consisting of research, development, and frontend teams.
• Proven time management skills to handle dynamic and agile development situations, ensuring timely delivery of solutions in a fast-paced environment.
• Self-motivated contributor who frequently takes initiative to enhance the codebase and share best practices, contributing to the development of an open, transparent, and collaborative engineering culture.