IN_Manager_AI Engineer_Data and Analytics_Advisory_Noida
Tasks
- Build APIs for model inference at scale
- Deploy ML services using Kubernetes and Docker
- Design ML pipelines for experimentation and model management
- Develop LLM serving and distributed training
- Implement DevOps and LLMOps workflows
- Optimize fine tuning latency and accuracy
- Orchestrate LLM workflows using LangChain and LangGraph
- Reduce training and resource requirements
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Bedrock | Amazon Aurora | Amazon RDS | Amazon SageMaker | Azure AI | Azure Cosmos | Azure Cosmos DB | Cloud Spanner | Cosmos DB | DeepSpeed | Distributed Training | Docker | DynamoDB | Fine Tuning | Flowise | Fluentd | Google BigQuery | Google Cloud | Google Cloud Spanner | Grafana | Hugging Face | Hugging Face Transformers | JavaScript | Keras | Kibana | Kubernetes | LLMOps | LangFlow | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | MLflow | Model Fine-tuning | MongoDB | MySQL | PostgreSQL | Prometheus | PyTorch | Python | SQL | TensorFlow | VLLM | Vertex AI
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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