Staff Machine Learning Engineer (Employer of Record)
Tasks
- Architect LLM-powered solutions
- Build evaluation frameworks for LLM performance
- Build retrieval-augmented generation pipelines
- Create recommendation and personalization systems
- Design and deploy machine learning solutions
- Develop multi agent GenAI systems
- Engineer ML and GenAI platform and inference pipelines
- Implement parameter efficient fine tuning
- Implement scalable secure data and feature systems
- Integrate LLM solutions with enterprise architecture
- Mentor machine learning engineers
- Troubleshoot and improve ML system reliability and scalability
Perks/Benefits
- N/A
Skills/Tech-stack
Agent systems | Apache Airflow | Apache Spark | Causal Inference | Contextual compression | DAG | DVC | Data Pipelines | Databricks | Databricks Model Serving | Deep learning | Efficient Fine Tuning | Fine Tuning | Flink | GRPC | Generative AI | Graph Neural Networks | GraphQL | Hybrid retrieval | Inference Pipelines | Kafka | Kinesis | Kubeflow | Langchain | Language Models | Large Language Models | Llamaindex | LoRA | MLflow | Machine Learning | Model Evaluation | Model Monitoring | Model Serving | Multi-Agent | Multi-Agent Systems | Neural Networks | PEFT | Parameter efficient fine-tuning | Python | Q LoRA | Query Expansion | RAG | Ray | Recommendation Systems | Reranking | Retrieval-Augmented Generation | Snowflake | Transformers | Vector Databases
Education
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