IN_Senior Associate_GEN AI_Data and Analytics_Advisory_Noida
Tasks
- Deploy models with Kubernetes and Docker
- Design ML pipelines
- Develop model inferencing APIs at scale
- Fine-tune and optimize LLMs
- Implement MLOps and LLMOps practices
- Manage experiments and models
- Optimize latency and accuracy
- Reduce training and resource requirements
- Support distributed training and serving
- Use GPU architecture knowledge for training and serving
- Use data visualization and statistical modeling
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS Bedrock | Amazon RDS | Amazon SageMaker | Aurora | Azure | Azure AI | Cosmos DB | Data parallelism | Deep learning | DeepSpeed | Distributed Training | Docker | DynamoDB | Flowise | Fluentd | GPU Architecture | Generative AI | Google BigQuery | Google Cloud | Grafana | Hugging Face | JavaScript | Keras | Kibana | Kubernetes | LLM | LLMOps | LangFlow | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | ML Ops | MLflow | Machine Learning | Model Parallelism | MongoDB | MySQL | PostgreSQL | Prometheus | PyTorch | Python | SQL | Spanner | TensorFlow | VLLM | Vertex AI
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