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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and managing RAG pipelines, fine-tuning LLMs, and developing automated evaluation frameworks. Proficient in advanced embedding models and vector databases, with a strong focus on prompt optimization and parameter-efficient fine-tuning.
Highest-signal resume keywords
RAG Pipeline ManagementFine-Tuning with PEFT/LoRAPython ProgrammingVector Database ExpertisePrompt Optimization
ATS Keywords
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Hard Skills
Machine LearningArtificial IntelligenceDeep LearningAdvanced Embedding ModelsParameter-Efficient Fine-TuningPrompt EngineeringContext Precision MeasurementRecall MeasurementMetadata FilteringHybrid Search Strategies
Soft Skills
LeadershipMentoringCollaboration
Tools & Technologies
PythonPyTorchTensorFlowLangChainLlamaIndexPineconeMilvus
Industry Keywords
RAGLLMsPEFTLoRAHyDEParent-Document Retrieval
Tech Stack
Tools & technologiesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Design and manage complex, multi-stage RAG pipelines ensuring low latency and high relevance
- Lead fine-tuning initiatives using PEFT/LoRA for open-source models to improve domain-specific task performance
- Develop automated evaluation frameworks such as RAGAS to measure LLM accuracy, context precision, and recall
- Architect metadata filtering and hybrid search strategies within vector databases such as Pinecone and Milvus
- Guide junior analysts in prompt engineering, chunking strategies, and code quality
Requirements
What you’ll need- Bachelor's/Master’s in CS/Data Science
- 4–7 years in ML/AI
- 1+ years specifically working with LLMs
- Python
- PyTorch/TensorFlow
- LangChain
- LlamaIndex
- Advanced embedding models
- Deep expertise in advanced RAG, including HyDE and parent-document retrieval
- Prompt optimization
- Parameter-efficient fine-tuning
- Experience with PEFT/LoRA
- Knowledge of vector databases such as Pinecone and Milvus