MLOps Engineer
4 weeks ago
About the Company:
This is a Series A funded web3 startup based in Singapore with a team of over 60. Three founders lead the company with experience in successful ventures. They have two products: a fantasy trivia game and a recently launched, pseudonymous social media app. This social media app is designed to be a platform for honest opinions on unconventional and esoteric topics, similar to a blend of Reddit and Twitter. The company utilizes a variety of technologies for development, including Solidity, NodeJS, MEAN/MERN stacks, mobile development frameworks like Ionic or Flutter, and cloud technologies like Design, build, and maintain end-to-end ML training and serving pipelines
- Create template repos. for ML model training and serving
- Design serving architectures to achieve the required latency and throughput
- Implement CI/CD tools to automate model training, testing, and deployment
- Design, build, and maintain scalable infrastructure for deploying machine learning models into production environments.
- Optimize and fine-tune the infra. for performance, scalability, and cost efficiency
- Ensure consistency of pipeline runs/API responses irrespective of the run environment by proper environment/config./secrets management
- Ensure traceability and reproducibility of pipeline runs/API responses by implementing best practices and tools for logging, code/data/model/metadata versioning, etc.
- Ensure reliability of pipelines/APIs by implementing monitoring and alerting tools
- Collaborate closely with AI scientists to develop and deploy high-performance ML APIs that are extensible and optimized for efficiency and performance
- Provide mentorship and guidance to the DevOps team on ML-specific architectures, tools, and best practices.
What you should have:
- At least 3 years of experience specifically in productionizing and maintaining ML pipelines
Experience in below mentioned or similar tools and platforms
- Docker, Kubernetes for containerization and orchestration
- Jenkins, Github Actions, CircleCI for CI/CD
- MLFlow, Neptune, and TensorBoard for ML experiment tracking and monitoring
- ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana for general logging and monitoring
- Airflow, perfect for workflow management
- Terraform for IaaS
- AWS solutions and services
- Understanding of how ML models and workflows work - enough to be able to debug them if required
What we have to offer :
- Competitive financial rewards + annual PLI (Performance Linked Incentives)
- Hybrid mode of working with the base location being New Delhi
- Meritocracy-driven, candid & diverse culture.
- Company trips and virtual bonding activities.
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