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Machine Learning Engineer

CÔNG TY TRÁCH NHIỆM HỮU HẠN MỘT THÀNH VIÊN RAMCO SYSTEM VIỆT NAM

Hết hạn: 22/03/2024

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Machine Learning Engineer
Job Code: Machine Learning Engineer
Qualification (Educational): Bachelor's or Master's degree in Computer Science, Machine Learning or a related field.
Location Country: India
Years of Experience: 3+
Skill Sets Required:
  • Minimum of 3 years of hands-on experience in machine learning and deep learning model development and deployment.
  • 3 years+ of experience manipulating data sets and building deep learning models, with a Bachelor's or Master's degree in Statistics, Mathematics, Computer Science or other quantitative field.
  • Strong programming experience, with solid understanding of software engineering design patterns and best practices.
  • Experience with SQL & NoSQL databases such as MongoDB is an advantage.
  • Knowledge of a variety of machine learning techniques (clustering, recommender systems, Natural Language Processing, Deep learning etc.) and their real-world applications.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.).
  • Experienced in the use of CI/CD and DevOps tools such as Jira, Jenkins, GIT/Bitbucket.
  • Ideally has track record of deploying Machine Learning solutions into Production at scale.
  • Experience with containerization technologies (e.g., Docker, Kubernetes).
  • Excellent problem-solving skills and the ability to work independently as well as in a collaborative team environment.
Projects: Showcase 2 or more projects involving the development and deployment of machine learning and deep learning models. Highlight the challenges faced, solutions implemented, and the impact of the projects.
Benefits: · Competitive salary · Professional development opportunities · Dynamic and collaborative work environment If you are passionate about machine learning, possess advanced deep learning skills, and thrive in a collaborative environment, we encourage you to apply for this exciting opportunity. Join us in shaping the future of technology and making a meaningful impact with your skills and expertise.
Roles & Responsibilities:
Position Overview: As a Machine Learning Engineer at Ramco Systems, you will play a crucial role in developing and implementing advanced machine learning and deep learning models including LLMs. The ideal candidate will have a minimum of 3 years of hands-on experience in machine learning, with a strong focus on deep learning techniques. A background in computer science and a passion for creating intelligent solutions that have a real-world impact are essential.
Responsibilities:
  • Implement and deploy machine learning and deep learning models to solve complex business challenges. Conduct exploratory data analysis and feature engineering to extract meaningful insights from large structured & unstructured datasets.
  • Evaluate and fine-tune deep learning models, including transformer models, for optimal performance, scalability, and efficiency.
  • Stay abreast of the latest advancements in machine learning and deep learning and contribute to the adoption of new technologies within the team.
  • Collaborate with data engineers to build and maintain scalable data pipelines for model training and inference.
  • Work closely with data scientists and stakeholders to understand project goals and requirements.
  • Contribute to the development and maintenance of a robust and scalable machine learning infrastructure
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Thông tin chung

  • Ngày hết hạn: 22/03/2024
  • Thu nhập: Thỏa thuận
Giới thiệu công ty Xem trang công ty
Ramco is a fast growing enterprise software player disrupting the market with its multi-tenant cloud, mobile and BOT-enabled enterprise software in HCM and Global Payroll, ERP and Aviation M&E/ MRO. Part of the USD 1 billion Ramco Group, Ramco Systems focuses on Innovation and Culture to...
Quy mô công ty
Từ 501 - 1000 nhân viên
CÔNG TY TRÁCH NHIỆM HỮU HẠN MỘT THÀNH VIÊN RAMCO SYSTEM VIỆT NAM
Địa chỉ công ty: Ho Chi Minh City

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