Python Engineer

Harvey Nash; Remote; GBP 100,000 - 125,000; Part time

Job summary

A leading tech consultancy is looking for a Python Engineer for a 6-month contract (outside IR35) based in the UK. The role focuses on implementing AI/ML solutions for automation in a remote work setting. Key responsibilities include developing OCR components, leveraging cloud services like AWS, and driving innovative automation projects. Candidates should have strong expertise in Python (3.9+), experience with AI/ML technologies, and a collaborative mindset. Starting ASAP.

Qualifications

• Experience with OCR, Object Detection, and LLM implementation.
• Proficiency in core Python (3.9+) with understanding of OOP.
• Knowledge of data structures and algorithms for optimization.
• Familiarity with AWS services including Lambda and S3.
• Experience in API development using FastAPI.

Responsibilities

• Enhance the automation service using AI/ML technology.
• Develop components for OCR and LLM analysis.
• Provide operational support and maintain monitoring.
• Drive R&D for innovative automation solutions.
• Collaborate in team events and communities of practice.

Skills

AI/ML technology usage

OCR technology

Object Detection

LLM analysis

Core Python

Data processing with Pandas

Cloud services integration

API Development with FastAPI

Agile methodologies

Tools

Transformers/Hugging Face

PyTorch

OpenCV

PIL/Pillow

YOLO frameworks

Grafana

Apache Airflow

MLflow

Job description

Python Engineer | 6 Month Contract | (Outside IR35) | Remote, Edinburgh | Starting ASAP

Day Rate: Market Rates

About the role

The organisation has restructured its IT software delivery to align with key business domains, aiming for enduring development teams with clear product ownership. A dedicated team has been formed to advance digital registration automation by analysing the problem domain and developing solutions for high-volume, low-complexity casework.

Key challenges include

• Using OCR and Large Language Models (LLMs) to assess automation risk in deed documents.
• Applying LLMs to interpret unstructured content in title sheets for more complex casework.
• Automating wet signature verification through document analysis, object detection, and open language models.

The team focuses on improving the scope and accuracy of automation solutions, working closely with other business domains to ensure integration with existing platforms and alignment of roadmaps for production deployment.

Main Duties

Enhance and expand the production automation service using OCR, Object Detection, and LLM AI for land register applications.

Key responsibilities

• Develop components for deed OCR, object detection, and LLM-based title analysis.
• Conduct research and spikes to broaden automation scope.
• Provide high-quality operational support and maintain robust monitoring, dashboards, and deployment processes.
• Drive R&D for innovative automation solutions.
• Ensure code quality, testing, and compliance with non-functional requirements (security, performance, accessibility).
• Troubleshoot issues across modern AWS stacks and legacy systems.
• Apply collaborative practices (pairing, mobbing, code reviews) and actively engage in team events and wider communities of practice.

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 audiences
• 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

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

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.