Python Engineer – Automation | 6 Month Contract | (Outside IR35) | Hybrid, Edinburgh| Starting ASAP
Day Rate:
Harvey Nash’s Pub Sec client is restructuring its software delivery model to create long‑standing development teams aligned to core business domains, ensuring clear ownership of digital products. The Land Registration Automation team is responsible for analysing the problem space and developing solutions that enable high‑volume, low‑complexity casework to be automated. Applying OCR and large language models to deed documents to assess automation risk. * Using large language models to interpret unstructured Title Sheet content and support automation of more complex cases. * Automating wet signature verification using document analysis, object detection techniques and open language models.
The team is focused on expanding the scope, accuracy and reliability of the automation capability. They work closely with operational and customer‑facing teams to ensure solutions integrate effectively with existing digital platforms and align with wider delivery roadmaps.
Commercial experience with AI/ML technology: OCR, Object Detection and LLM analysis implementation Machine Learning & AI Libraries including: PyTorch for deep learning model development and training OpenCV for computer vision tasks and image preprocessing in object detection Core Python Skills: Proficiency in Python 3.9+ with understanding of object-oriented programming, decorators, context managers, and async/await patterns Data structures and algorithms for efficient data processing and model optimization Data Processing: Pandas and NumPy for data manipulation, cleaning, and numerical operations SQLAlchemy or psycopg2 for database connectivity and ORM operations Boto3 for AWS service integration and automation AWS (working within Technical Lead’s architecture): CloudWatch logging and metrics for monitoring and debugging Experience with CDK for infrastructure deployment EKS/ECS/Kubernetes for containerized AI deployments FastAPI for building REST APIs and model serving endpoints Authentication/authorization implementation (JWT, OAuth) Software Development: Making excellent quality AI/ML software collaboratively with other engineers Working effectively under technical leadership while contributing specialized AI/ML expertise Design and implementation of AI/ML solutions using service-based and serverless architecture Using written, verbal, and visual communication to explain AI/ML concepts to both technical and non-technical audience Cloud monitoring, telemetry, intelligence tools for AI/ML systems, including Grafana Experience working in Agile delivery models – Scrum and/or Kanban frameworks Formal XP engineering techniques including TDD and pair programming Advanced AI/ML Technologies: Custom model architecture design and implementation Multi-modal AI systems combining text, image, and structured data Production ML Systems: Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management Real-time model serving and edge deployment strategies A/B testing frameworks for ML model evaluation…