Principal ML Engineer
INR 2500K-4500K (estimate) Senior-level Full Time
Tasks
- Build and operate RAG and agentic AI systems
- Build observability dashboards for model performance
- Create scalable ML pipelines
- Deploy and monitor ML and GenAI models
- Design ML and GenAI platforms
- Develop reusable AI platform services and APIs
- Establish LLMOps practices
- Establish responsible AI governance
- Implement CI CD for ML and model versioning
- Implement evaluation frameworks for ML and GenAI
- Integrate ML and GenAI with Snowflake
- Manage experimentation and A/B testing
- Mentor engineers and data scientists
- Optimize GenAI workloads for latency and cost
- Productionize enterprise ML solutions
- Translate business problems into AI products
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | A2A | AWS Fargate | AWS Lambda | Agent systems | Amazon Bedrock | Amazon EC2 | Amazon S3 | Amazon SNS | Amazon SQS | Amazon SageMaker | Apache Airflow | B testing | BentoML | CI/CD | Cortex | Customer Segmentation | Data Quality | Docker | Embeddings | Experimentation | FastAPI | Kafka | Keras | Kubernetes | LLMOps | Langfuse | Langsmith | MCP | MLflow | Marketing Mix | Marketing Mix Modeling | Microservices | Mix Modeling | Model Governance | Model Monitoring | Model Registry | Multi-Agent | Multi-Agent Systems | NLP | NumPy | Observability | PGVector | Pandas | Polars | Prompt engineering | Python | RAG | Ray | Retraining | SQL | Scikit-learn | Series forecasting | Snowflake | Snowflake APIs | Snowpark | Statistical modeling | TensorFlow | Terraform | Time Series | Time Series Forecasting | Tool-Calling | VLLM | Vector Search | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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