Python Engineer - Automation | 6 Month Contract | (Outside IR35) | Hybrid, Edinburgh| Starting ASAP

Day Rate: £

About the Role:

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.

Key challenges include:

  • 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.

Essential Skills & Experience:

Commercial experience with AI/ML technology:

  • OCR, Object Detection and LLM analysis implementation
  • Machine Learning & AI Libraries including:
    • Transformers/Hugging Face for working with pre-trained LLMs, fine-tuning, and inference
    • PyTorch for deep learning model development and training
    • OpenCV for computer vision tasks and image preprocessing in object detection
    • PIL/Pillow for image manipulation and format conversion
    • YOLO object detection frameworks

  • 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
    • Error handling and debugging using try-catch blocks, logging, and debugging tools
    • 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):
  • Lambda function development with proper event handling and response formatting
  • S3 operations including multipart uploads, presigned URLs, and event notifications
  • CloudWatch logging and metrics for monitoring and debugging
  • Understanding of IAM and security for role-based access and credential management
  • Experience with CDK for infrastructure deployment
  • SQS for message queuing
  • EKS/ECS/Kubernetes for containerized AI deployments
  • API Development:
  • FastAPI for building REST APIs and model serving endpoints
  • Requests library for HTTP client operations and external API integration
  • 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
    • Development Practices:
    • 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
    • Working within defined infrastructure-as-code frameworks

Desirable Qualifications
Advanced AI/ML Technologies:

  • Custom model architecture design and implementation
  • Advanced fine-tuning techniques including LoRA, QLoRA, and parameter-efficient methods
  • Multi-modal AI systems combining text, image, and structured data
  • Reinforcement Learning from Human Feedback (RLHF) for model alignment

Production ML Systems:

  • Apache Airflow/Dagster for ML workflow orchestration and ETL pipeline management
  • Model versioning and experiment tracking (MLflow, Weights & Biases)
  • Real-time model serving and edge deployment strategies
  • A/B testing frameworks for ML model evaluation

This role has been deemed Outside IR35 by the client. Applicants must hold, or be happy to apply for, a valid Basic Disclosure Scotland. Please click the link to apply.

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