LLM Engineer

LLM Engineer – Geotechnical Data Digitization Location: Remote Engagement Type: Contract (Full-Time, 40 hours/week) Duration: an initial 8-10 weeks, with strong likelihood for extension into future phases (Up to 1 year in length) Role Overview: We are seeking a highly skilled LLM Engineer to assist in the development of a multi-modal Large Language Model (LLM) pipeline for digitizing geotechnical bore log data. This role is critical to transforming unstructured PDF documents into structured, machine-readable JSON outputs that support downstream analytics, GIS integration, and AI-powered search. You will work closely with a Project Manager and technical stakeholders at our customer to build, fine-tune, and evaluate a custom LLM solution capable of interpreting complex geotechnical documents across multiple vendors. Key Responsibilities: Phase 1 – Pilot Development • Fine-tune a multi-modal LLM (e.g., Pixtral-12B, PaliGemma, Gemma 3) using annotated bore log PDFs and JSON samples. • Build preprocessing pipelines for: Page segmentation, Figure isolation, Normalization of units and soil classification. • Develop and implement an evaluation framework including Precision/Recall/F1, domain-specific metrics, and JSON schema conformance. Cross-Vendor Generalization • Test model generalization on bore logs from 3 additional vendors. • Identify and categorize failure cases. • Compare performance across vendors and recommend strategies for scaling. Pipeline Packaging & Handoff • Package preprocessing scripts, model artifacts, and evaluation dashboards into a reproducible workflow. • Deliver structured JSON outputs and final benchmark reports. • Provide all source code and documentation for handoff. Required Qualifications: • Proven experience fine-tuning and deploying multi-modal LLMs (e.g., Pixtral, LLaMA, Gemma, etc.) • Ollama/llama.ccp, mongodb/non-relational dbs, and ai coding tools (cursor/windsurf/co-pilot.) experience. • Experience using OSS models • Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow) • Experience with OCR, image preprocessing (OpenCV), and document parsing • Familiarity with geospatial data and JSON schema design • Ability to work with GPU environments (e.g., A100s) and cloud-based training setups • Strong understanding of evaluation metrics and model benchmarking • Excellent communication and documentation skills Preferred Qualifications (nice to have): • Experience with geotechnical or engineering datasets • Familiarity with MongoDB, vector search, and embedding-based retrieval • Exposure to MLOps practices and CI/CD for ML pipelines • Prior work in AI document ingestion or enterprise-scale data transformation