Machine Learning Engineer I - Large Language Models - AI & Human Health Research
Tasks
- Build ETL pipelines for clinical data
- Build LLM evaluation harnesses with red teaming
- Build ML observability dashboards
- Build data infrastructure for model benchmarking and stress testing
- Build retrieval-augmented generation pipelines
- Design automated data preparation and lineage
- Design build and deploy LLM powered clinical applications
- Develop agentic multi agent workflows and tool orchestration
- Engineer pre deployment model validation and post deployment monitoring
- Ensure HIPAA compliance and patient safety validations
- Ensure data integrity reproducibility and fairness
- Fine tune and adapt foundation models
- Implement prompt engineering and prompt evaluation workflows
- Operationalize LLM deployment with inference optimization
- Set up model tracking and drift detection
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Autogen | Azure | Bias Evaluation | C# | C++ | CI/CD | CrewAI | Data Engineering | Development Lifecycle | Docker | Drift Detection | ETL | Faiss | GCP | Git | Hadoop | Hallucination detection | Instruction Tuning | Java | Kubeflow | Kubernetes | Langchain | Langgraph | Llamaindex | LoRA | MLflow | Machine Learning | Milvus | Model Drift | Model Drift Detection | Model Monitoring | Model Validation | PEFT | PGVector | Pinecone | Prompt engineering | PyTorch | Python | QLoRA | Red Teaming | Retrieval-Augmented Generation | SQL | Scala | Semantic Kernel | Software Development Lifecycle | Software development | Spark | Supervised Learning | TensorFlow | Unsupervised Learning | Vector Databases | Weaviate
Education
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