IN_Manager_AI Engineer_Data and Analytics_Advisory_Noida
Tasks
- Architect GPU based training
- Build model inference APIs
- Containerize applications with Docker
- Deploy models with Kubernetes
- Design machine learning pipelines
- Fine-tune and optimize models
- Implement DevOps and MLOps practices
- Manage machine learning experiments
- Manage models and features
- Reduce latency and resource usage
- Retrain machine learning models
- Run distributed training
- Serve large language models
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Bedrock | Amazon Aurora | Amazon DynamoDB | Amazon RDS | Amazon SageMaker | Amazon Web Services | Azure | Azure AI | Container Orchestration | Cosmos DB | Data parallelism | Deep learning | DeepSpeed | Distributed Training | Docker | Flowise | Fluentd | GPU Architecture | Google BigQuery | Google Cloud | Google Spanner | Grafana | Hugging Face | JavaScript | Keras | Kibana | Kubernetes | LLM serving | LLMOps | LangFlow | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | ML Engineering | MLflow | Machine Learning | Microsoft Cosmos DB | Model Parallelism | MongoDB | MySQL | PostgreSQL | Prometheus | PyTorch | Python | SQL | TensorFlow | VLLM | Vertex AI | Web Services
Education
Roles
AI | AI Engineer | Data Science | Data Science Manager | Engineer | Learning Engineer | Machine Learning Engineer | Manager
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