MLOps Engineer
Tasks
- Automate ML pipeline workflows
- Collaborate with Product and DevOps teams
- Collaborate with generative ai researchers
- Communicate model performance updates
- Create alerting systems
- Deploy machine learning models
- Design large language model pipelines
- Design machine learning pipelines
- Detect data drift
- Implement model deployment
- Manage cloud infrastructure for machine learning workloads
- Optimize model performance
- Perform data preprocessing
- Run truth analysis for model outputs
- Set up model monitoring
- Train and tune machine learning models
Perks/Benefits
- Career plan
- Discounted lunch options
- Educational budget
- Flexible remuneration
- Flexible working hours
- Hybrid opportunity
- Indefinite contract
- Language classes
- Private health insurance
- Relocation package
- Work visa sponsorship
Skills/Tech-stack
AWS | Azure | Azure CycleCloud | Azure Data | Azure Data Factory | Azure Data Lake | Azure Kubernetes | Azure Kubernetes Service | Azure Machine Learning | Azure Managed | Azure Managed Lustre | Azure Monitor | Azure RBAC | CI/CD | Chunking | Context Based Retrieval | Data Factory | Data Lake | Data parallelism | Deep learning | DeepSpeed | Docker | FSDP | Generative AI | GitOps | Hyperparameter Tuning | Kubernetes | Kubernetes Service | Language Models | Large Language Models | Machine Learning | MegatronLM | Mixture of Experts | Model Parallelism | Python | Ray | Retrieval-Augmented Generation | Streaming | Vectorization
Education
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