- Design and implement end-to-end RAG pipelines integrating proprietary chunking algorithms, embedding models, vector databases, and data retrieval agents
- Build and optimize retrieval systems over large-scale proprietary datasets using advanced embedding techniques
- Develop LLM-based solutions orchestrating retrieval, generation, and ranking for high-quality, context-aware responses
- Investigate and solve challenges in vector search, chunking and indexing strategies, unstructured data retrieval evaluation, and GraphRAG
- Work with Product and Design teams to build ML-based solutions that enhance user experiences and meet business objectives
- Collaborate with ML Operations to automate management of the full ML systems lifecycle from technical design through implementation
Requirements
- Bachelor's degree or higher in Computer Science, Engineering, or a related field
- 3+ years of hands-on industry experience with machine learning, NLP, information retrieval systems, and large-scale text processing
- Experience designing, shipping, and maintaining production systems
- Strong Python programming skills
- Working knowledge of data processing tools and ML frameworks such as PyTorch, Transformers, and HuggingFace
- Experience with LLM orchestration libraries/frameworks such as LangChain and LlamaIndex
- Experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation
- Experience with vector databases such as PostgreSQL/PGVector, OpenSearch, and Pinecone
- Understanding of similarity search techniques and vector indexing algorithms
- Effective coding, documentation, collaboration, and communication habits
- Strong problem-solving skills and a proactive approach
- Ability to adapt to a fast-paced and dynamic work environment
Core Competencies
Demonstrates expertise in designing and implementing end-to-end RAG pipelines, leveraging advanced machine learning techniques and frameworks to optimize retrieval systems and enhance user experiences. Proficient in Python programming and familiar with vector databases and LLM orchestration libraries.
Highest-signal resume keywords
- Machine Learning
- Natural Language Processing
- Python Programming
- LLM Orchestration
- Vector Databases
ATS Optimization Keywords
Hard Skills
- Machine Learning
- Natural Language Processing
- Information Retrieval Systems
- Large-Scale Text Processing
- Python Programming
- Data Processing
- ML Pipelines
- Vector Indexing Algorithms
- Chunking Algorithms
- Embedding Models
Soft Skills
- Problem-Solving
- Collaboration
- Communication
- Adaptability
- Proactive Approach
Industry Keywords
- RAG Pipelines
- Data Retrieval
- Similarity Search
- Unstructured Data Retrieval
- ML Systems Lifecycle
Tools & Technologies
- PyTorch
- Transformers
- HuggingFace
- LangChain
- LlamaIndex
- PostgreSQL/PGVector
- OpenSearch
- Pinecone