AI Engineer - Engineering Change Request Processing (Contract)

Position Overview DRAS Consulting Ltd is a fully funded company focused on building exciting AI products. We have a potential client for whom we are developing a demonstrator product for a specific engineering work-flow. We are seeking an experienced AI Engineer to develop and implement an intelligent document processing system that extracts critical technical information from complex Engineering Change Requests (ECRs). This role involves building end-to-end AI solutions that transform unstructured engineering documentation into structured data for CAD and manufacturing systems integration. Integration with CAD is not within scope as of now, we just need a suitable output format. Key Responsibilities Core Technical Requirements ●       Document Intelligence: Process multi-modal Engineering Change Requests containing free-flowing text, technical drawings, labelled/unlabelled images, part numbers, and specifications ●       Information Extraction: Develop AI systems to identify and extract specific technical changes including: o   Part modifications and revisions o   New part additions with specifications o   Part deletions and discontinuations o   Technical parameter changes ●       Technical Implementation ●       Input: Engineering Change Request in PDF format comprising text and images of engineering drawings ●       Computer Vision: Implement OCR solutions for technical drawings and image analysis for unlabelled component recognition ●       Natural Language Processing: Build NLP pipelines to parse technical documentation and extract structured engineering data ●       Generative AI: Leverage large language models (LLMs) / Small language Models (SLMs) and multimodal AI for complex document understanding and data extraction. Fine-tune the model to ensure that the output is as close to deterministic as possible and minimise hallucinations. Required Qualifications Technical Expertise ●       Generative AI: Experience with LLMs / SLMs, prompt engineering, integrating with LLMs/SLMs within code. Fine-tuning the model using suitable methods to ensure as close to deterministic output. ●       Computer Vision: Proven experience with image processing, OCR (e.g. Tesseract, AWS Textract, Google Vision API), and object detection frameworks ●       Natural Language Processing: Strong background in text processing, named entity recognition, and document classification ●       AI/ML Foundation: hands-on experience with machine learning and deep learning, ●       Programming & Tools ●       Languages: Python (required), with experience in suitable AI libraries e.g. TensorFlow/PyTorch, OpenCV, spaCy/NLTK, Hugging Face Transformers ●       Cloud Platforms: GCP ML services (preferred) since we will be using that. However, experience on Azure / AWS is acceptable, we just need to factor in the learning curve. ●       Version Control: Git, MLOps practices, and model deployment pipelines Key Competencies ●       Problem-Solving: Ability to tackle complex, unstructured data challenges ●       Accuracy Focus: Strong attention to detail for engineering data ●       Collaboration: Work effectively with engineering teams and domain experts ●       Innovation: Stay current with emerging AI technologies and best practices ●       Communication: Explain complex AI concepts to technical and non-technical stakeholders Success Metrics ●       Achieve >90% accuracy in technical information extraction ●       Reduce manual ECR processing time ●       Maintain high system reliability and performance standards What We Offer ●       A short-term flexible contract to work with experienced domain specialists and a technical advisor to start with. It could lead to a longer-term opportunity subject to the success of this phase. ●       We are based in Bristol but are happy for the candidate to work remotely, with possible visits to Bristol twice a month. This role offers the unique opportunity to apply state-of-the-art AI technologies to solve real-world engineering challenges, directly impacting manufacturing efficiency and product development cycles. Please provide a brief para about your product and better still,a website / Github repository.