LevelAI Hiring Drive 2024: LevelAI Hiring Drive 2024 for its MLOps Engineer.LevelAI Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a bachelor’s in Computer Science. if you are interested, please apply as soon as possible.
Company Name: LevelAI
Role: MLOps Engineer
Location: Remote
Experience: 2 years
Qualification: Bachelors in Computer Science
Batch: Any
Job Type: Full Time
Salary: Best in Industry
LevelAI Hiring Drive 2024
Job Description:
- As a critical member of the team, your work will be cutting-edge technologies and will play a high-impact role in shaping the future of AI-driven enterprise applications.
- You will directly work with people who’ve worked at Amazon, Facebook, Google, and other technology companies in the world.
- With Level AI, you will get to have fun, learn new things, and grow along with us.
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Requirements:
- Bachelors in Computer Science or related fields.
- Minimum 2 years experience in DevOps role combined with developing deployment/serving pipelines and orchestrating the deployment of ML models for production-grade systems.
- Minimum 1-year experience in Kubernetes, Docker, CI/CD tools, Prometheus, preferably working with ML models.
- Minimum 1 year of experience with ML Operations tools such as KubeFlow, MLFlow, Metaflow, or Sagemaker.
- Experience with Triton inference server, SeldonCore, K-Serve and related tool stack is a huge bonus.
- Demonstrated experience serializing/exporting and serving deep learning models at scale, and deploying using modern ML platforms.
- Strong troubleshooting skills to keep our ML models highly available.
- You have a passion for learning and adopting new technologies with a focus on finding the right tool for the job, and a sense of ownership of your work.
- Self-starter with excellent verbal and written communication and presentation skills.
Responsibility:
- Create and develop ML Operations Pipelines which allow for controlled and continuous enhancement of existing and new ML features and training pipelines.
- Design, build, and develop state of art machine Learning system infrastructure core components and architect platforms to create, train and deploy ML models.
- Automate the operational support for model training and model serving pipelines.
- Build operating dashboards and charts to track system errors, performance and enable root cause analysis.
- Identify gaps and evaluate relevant tools and technologies as needed to improve processes and systems, leveraging open-source and cloud computing technologies to build effective solutions.
- Collaborate with the AI team to drive ML projects from conception to completion and production monitoring.
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