Ema Recruitment 2024 for freshers | Software Engineer

Ema  hiring freshers. Graduate in any Discipline candidate can apply for off campus placements with Ema. Interested and eligible candidates are encouraged to apply before the specified deadline. The detailed eligibility criteria, application process, and registration information are provided below.

• A Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent practical experience.
• A foundational understanding of, or experience with, programming languages pertinent to backend development and machine learning, such as Python and Go.
• Basic knowledge of, or a keen interest in, machine learning, NLP, and language model integration within enterprise applications.
• Familiarity with database technologies, particularly those suitable for machine learning applications like PostgreSQL.
• An understanding of API development principles and either experience with or interest in learning about deploying NLP and machine learning models.
• Curiosity and a learning mindset regarding cloud technologies, containerization, and machine learning operations (MLOps).
• Strong analytical and problem-solving skills coupled with meticulous attention to detail.
• The ability to work collaboratively within a team as well as independently in a global team setting.

• Playing a crucial role in the development and upkeep of complex enterprise-level software applications, particularly focusing on backend systems, API development, and the integration of language models and NLP technologies.
• Contributing to the construction of scalable and reliable backend systems utilizing Go and Python, with a special emphasis on integrating and managing machine learning models.
• Assisting in the creation, upkeep, and optimization of APIs intended for NLP and machine learning applications, leveraging REST or GraphQL.
• Taking part in the establishment and maintenance of data schemas appropriate for machine learning applications within databases like PostgreSQL.
• Gaining proficiency in deploying and overseeing NLP models, including fine-tuning and monitoring for performance and accuracy.
• Engaging with cloud technologies and containerization tools (e.g., Docker, Kubernetes) to develop a scalable machine learning infrastructure.
• Contributing to ensuring the security of applications, particularly those dealing with sensitive language data and machine learning models.
• Staying updated with the latest advancements in NLP, language models, and machine learning technologies, and applying this knowledge to enhance our software development processes and methodologies.