Machine Learning Ops Engineer (Full time)

We are a global provider of end-to-end AI training solutions, incorporated in the US. We revolutionize the way AI teams build AI products. We provide both OTS, tailor-made datasets, and a comprehensive end-to-end platform offering AI teams to easily crawl and auto-label data, then auto-train and auto-deploy models in a production environment.
We invite you to become a part of our journey as we enhance our comprehensive AI platform. Our mission also includes integrating blockchain technology into our platform. This integration aims to bolster security and privacy, while also enabling a Parallelism Approach to support distributed, parallel training.
Our DNA is a globally distributed company that allows you to work fully remotely with flexible working hours, we always work with folks from all walks of life with no borders.
Job type: Flexible working hours from Monday to Friday, ensuring 40+ hours per week. We need A players, who can work like hell, if you prefer a 9-5 job, this might not be a suitable company for you.
Location: Work from anywhere.
Reporting line: Head of Product
Number of headcounts: 1-2
Your role & responsibilities
You will streamline and automate the machine learning lifecycle, including integration, testing, releasing, deployment, and infrastructure management, to ensure more efficient and effective deployment and maintenance of machine learning models in production environments. You will also collaborate with other peers to write tech documentations for the platform. Please note that we are not building specific models, but we build an end2end AI platform for AI engineers to build and deploy on it so your role is not to maintain any specific models.
Design, implement, optimize, test ML pipelines for different types of models on our platform. (As of now, our platform supports almost all types of AI/ML models)
Maintain and optimize our Continuous Integration/Continuous Deployment (CI/CD) pipelines and manage Kubernetes deployments.
Infrastructure Management: Manage and optimize the infrastructure required for our users to run their machine learning models on our platform
Automation of Workflows: Design and implement Automate various stages of the machine learning lifecycle, from data collection to model training and deployment on our platform.
Other tasks assigned by the Line Manager
Your skills & qualifications
Must have:
Bachelor's or Master's degree in Computer Science or related field.
Middle level candidates who have hands-on ML modeling experience, involving the entire process from conceptualizing ideas to developing, evaluating, and deploying models, with the added advantage of having expertise in monitoring and managing model performance after deployment.
2-3 years of experience with cloud providers such as AWS/ GCP/ AZURE/ Alibaba.
Strong expertise in designing efficient and well-architected microservices is crucial for this role.
Experience of building systems that serve many users, require low latency performance.
Ability to work independently and collaboratively in a team environment
Good communication and time-management skills
Ability to work remotely with discipline
Nice to have:
Experience in a start-up company is a plus.
A driven and hustle person
Benefits for you
Starting salary: Negotiated salary depending on experience.
Salary review quarterly depending on the performance.
Token bonus based on Company policies.
Work-from-home allowance: 50 USD/month.
Insurance support: 100 USD/month.
Birthday gift.
Holiday gift.
Year End Performance Bonus (Cash)
Year-end party.
Company anniversary party.
All-hands Company Trip each year.
Annual Health Check-up
Public holidays. Take time off and spend it with your family during your country's public national/regional/state holidays.
Annual leave: 12 days/year and to be pro-rata rated for the actual monthly working period for full-time staff. Applied after the probation.
Sick leave with pay: maximum 6 days/year, on top of the 12-day annual leave credit, for full-time staff. Applied after the probation.
Period leave: 1 day each month for female employees.
Personal leave policy for special cases.
Employee Stock Option Plan (ESOP) can be applied after a (1) year working contribution, depending on the Company's business results and the individual's performance.
Performance recognition and promotion opportunities for consistently good performance.
External/internal training programs.
Fully remote.
Flexible working hours, divide your working hour within a day and week to work then enjoy your work-life balance style.
Startup working environment with young and talented people across the globe.
The opportunity to meet and work with global professionals around the world to expand your network.