Machine Learning Engineer  

***Responsibilities:***

1. Contribute to the development of software and solutions, emphasizing ML/NLP as a key component, to productize research goals and deployable services.

2. Collaborate closely with the frontend team and research team to integrate machine learning models into deployable services.

3. Utilize and develop state-of-the-art algorithms and models for NLP/ML, ensuring they align with the product and research objectives.

4. Perform thorough analysis to improve existing models, ensuring their efficiency and effectiveness in real-world applications.

5. Engage in data engineering tasks to clean, validate, and preprocess data for uniformity and accuracy, supporting the development of robust ML models.

6. Stay abreast of new developments in research and engineering in NLP and related fields, incorporating relevant advancements into the product development process.

7. Actively participate in agile development methodologies within dynamic research and engineering teams, adapting to evolving project requirements.

8. Collaborate effectively within cross-functional teams, fostering open communication and cooperation between research, development, and frontend teams.

9. Actively contribute to building an open, transparent, and collaborative engineering culture within the organization.

10. Demonstrate strong software engineering skills to ensure the reliability, scalability, and maintainability of deployable ML services.

11. Take ownership of the end-to-end deployment process, including the deployment of ML models to production environments.

12. 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.