Job
Description
Utilize expertise in AI/ML and Data Science to develop and deploy AI models in production environments, ensuring scalability, reliability, and efficiency. Implement and optimize machine learning algorithms, neural networks, and statistical modelling techniques to solve complex problems. Hands-on experience in developing and deploying large language models (LLMs) in production environments, with a good understanding of distributed systems, microservice architecture, and REST APIs. Collaborate with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment. Stay updated with the latest advancements in AI/ML technologies and contribute to the development and improvement of AI frameworks and libraries. Ensure compliance with industry best practices and standards in AI engineering, maintaining high standards of code quality, performance, and security. Required education Bachelor's Degree Preferred education Bachelor's Degree Required technical and professional expertise Proficiency in Python, C++. Experience with relevant ML libraries (e.g., TensorFlow, PyTorch) for developing production-grade quality products. Expertise in various databases including open-source databases like MongoDB, CouchDB, CockroachDB. Skills in Git, CI/CD pipelines (Jenkins, Travis CI, GitLab CI), and containerization (Docker, Kubernetes). Experience in contributing to open-source AI projects or utilizing open-source AI frameworks. Experience in optimizing AI algorithms for performance and scalability. Preferred technical and professional experience Proven ability to implement and optimize machine learning algorithms, neural networks, and statistical modelling techniques to solve complex problems effectively. Proficiency in distributed systems, microservice architecture, and REST APIs. Experience in collaborating with cross-functional teams to integrate MLOps pipelines with CI/CD tools for continuous integration and deployment, ensuring seamless integration of AI/ML models into production workflows. Experience in using container orchestration platforms such as Kubernetes to deploy and manage machine learning models in production environments, ensuring efficient scalability and management of AI infrastructure. Proven ability to contribute to the development and improvement of AI frameworks and libraries. Strong communication skills with the ability to communicate technical concepts effectively to non-technical stakeholders.
