Found Description
Responsibilities
- Build large scale recommendation systems utilizing embeddings and two-tower architectures
- Fine-tune and deploy embedding models (LLMs/SLMs) for multi-language text understanding and semantic search
- Architect and manage scalable MLOps and LLMOps infrastructure for model training, deployment, and monitoring
- Design and implement agentic systems for automated web extraction, NER, and entity resolution
- Build comprehensive evaluation frameworks for agent performance and data quality
- Collaborate cross-functionally with engineering and product teams to integrate models into production workflows
Requirements
- 6+ years of hands-on ML/NLP experience or 3+ years post-PhD/Master's
- Experience delivering revenue-impacting products in production environments
- Expertise in transformer stacks, prompt engineering, RAG systems, and vector-based retrieval
- Proven track recor...