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

Looking for extensive search/retrieval experience I have a working, well-tested RAG (Retrieval-Augmented Generation) system built 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 quality and add new capabilities. The work involves Upgrading existing components with newer algorithms backed by recent peer-reviewed research Adding an agentic retrieval layer with proper 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 paper-ready visualizations Writing clean, testable code 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 — real systems) Familiarity with latest retrieval research (2024-2026 papers) Ability to read a research paper and implement the key ideas cleanly Experience running proper ablation studies and benchmarks Strong Python, PyTorch, Hugging Face Transformers Apply tot his job Apply To this Job Apply To This Job