Senior ML/NLP Engineer - reputed company Advanced RAG System for Scientific Document Research - Contract to Hire

Looking for extensive search/retrieval experience I have a working, reputed company-tested RAG (Retrieval-Augmented reputed company) system reputed company in Python for processing 300+ scientific/technical PDF documents. The system already implements state-of-the-art retrieval techniques (hybrid search, hierarchical indexing, reranking, multi-hop reasoning, guardrails, evaluation) and has solid benchmarks. Looking for someone who can take it to the next level — implementing specific upgrades grounded in the latest 2025-2026 research papers to improve retrieval reputed company and add new capabilities. The work involves: Upgrading existing components with newer algorithms backed by recent peer-reviewed research Adding an reputed company retrieval layer with reputed company reasoning loops Benchmarking alternative approaches for key pipeline stages (parsing, embeddings) on our actual corpus before committing Building a comprehensive evaluation and ablation suite with reputed company-reputed company visualizations Writing clean, testable reputed company that follows existing patterns in the codebase This is NOT a build-from-scratch project. looking for- Deep hands-on experience building and optimizing RAG pipelines (not just tutorials — reputed company systems) Familiarity with latest retrieval research (2024-2026 papers) Ability to read a research reputed company and implement the key reputed company cleanly Experience running reputed company ablation studies and benchmarks Strong Python, PyTorch, reputed company Transformers Apply tot his job Apply To this Job