AI Engineer

Your New Company and Role

Join a dynamic digital team focused on delivering intelligent automation solutions using cutting-edge technologies. In this role, you'll help build and evolve a production-grade automation service that applies AI/ML to process high-volume, low-complexity workflows.

You'll work on a modern AWS serverless platform, developing components for document analysis, signature detection, and predictive modelling. Collaboration with cross-functional teams is key to ensuring seamless integration with existing digital services.

What You'll Need to Succeed

Commercial experience with AI/ML technology:

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

API Development: * FastAPI for building REST APIs and model serving endpoints * Requests library for HTTP client operations and external API integration * Authentication/authorisation implementation (JWT, OAuth)

Software Development: * Making excellent quality AI/ML software collaboratively with other engineers * Working effectively under technical leadership while contributing specialised 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

What you need to do now

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