Senior Research Engineer

Job Description & Requirements

We are seeking a talented and experienced Software Engineer with experience in MLOps/AI technology. In this role, you will play a key role in software development and focusing on deploying machine learning models, implementing MLOps best practices. You will work closely with our cross-functional research teams to streamline model deployment, improve scalability and reliability of systems.

The key roles and responsibilities include the following:

• Develop, test, and maintain enterprise system and applications, with a focus in ML/AI systems.
• Implement MLOps best practices to automate model training, deployment, and monitoring processes.
• Implement and optimize machine learning algorithms and deep learning architectures.
• Collaborate with data scientists and software engineers to integrate machine learning solutions into existing systems and deliver high-quality software products.
• Optimize AI pipelines for scalability, reliability, and performance.
• Conduct performance analysis and optimization to improve model efficiency and accuracy.
• Stay updated on the latest advancements in software engineering, AI/ML technologies and best practices.

Job Requirements

• Bachelor or Master Degree in Computer Science/Engineering or relevant disciplines
• Must have a minimum of 5 years of experience as a Software Developer/Engineer
• Hands-on experience in Cloud Native Technologies such as in micro-services, DevSecOps, and Containerisation Technologies
• Proficiency in designing and developing microservices and software systems using C# .NET, Python and angular
• A good understanding of CI/CD Pipelines in a distributed environment using Git, Artifactory, Jenkins, SonarQube, Docker registry, etc.
• Hands-on experience with deploying and managing microservices using container orchestration platforms (on-prem/public cloud)
• Excellent troubleshooting, follow-through, and problem solving skills, with attention to detail
• Excellent verbal and written communication skills
• Hands-on experience with machine learning frameworks/libraries
• Understanding of machine learning algorithms and techniques
• Familiarity with deep learning architectures and natural language processing (NLP) techniques.