AI/ML Engineer (George)

Environment

• A leading Insurance Company processes thousands of claims daily, each requiring thorough document review, validation, and processing. Their contact centre manages customer inquiries that follow predictable patterns.
• While their fraud detection currently relies on rules‑based systems, there is significant potential to enhance it with machine learning.
• They face real operational challenges that AI can address, and they need someone capable of solving them responsibly. This is not about building prototypes or demos; they are seeking a professional who can deploy AI into production and ensure it runs reliably. Key focus areas include claims document extraction, fraud detection, and customer service copilots.
• These are mission‑critical capabilities that must perform consistently every day. If you have experience shipping AI solutions to production and maintaining them, we want to hear from you.

Responsibilities

• Define the AI governance framework: how models get approved, tested, monitored, and retired
• Implement Azure OpenAI integrations with proper guardrails, content filtering, PII handling, audit logging
• Build the claims AI pipeline: OCR, document classification, entity extraction, validation
• Establish prompt engineering standards and build a reusable prompt library
• Create evaluation frameworks so they can measure accuracy, latency, and cost objectively
• Implement responsible AI controls because insurance data is sensitive and regulators are watching
• Optimise AI costs: token budgets, caching strategies, model selection (this stuff adds up fast)
• Support the Contact Centre Copilot implementation
• Roll out GitHub Copilot to the development team with appropriate policies and training
• Monitor model performance in production and catch drift before it causes problems

Requirements

What You Bring

• You’ve deployed AI/ML to production and maintained it. Jupiter notebooks don’t count
• 5+ years in software engineering with 2+ years focused on AI/ML
• Hands‑on Azure OpenAI or OpenAI API experience
• Strong Python skills for ML pipeline development
• Experience with document AI: OCR, form recognition, NER, document classification
• Understanding of LLM patterns: RAG, fine‑tuning, prompt engineering, embedding models
• Familiarity with vector databases and semantic search
• Software engineering discipline: testing, code review, documentation, monitoring

Education

• Degree or Diploma in Computer Science, Information Technology, Software Engineering, Computer Engineering, or a related technical field.
• Relevant cloud, API, or platform engineering certifications (Azure preferred) will be advantageous.
• Equivalent practical experience building and maintaining production APIs and distributed systems will also be considered.

Nice to Have

• Azure AI Services experience (Document Intelligence, Cognitive Services)
• Insurance claims processing knowledge
• Experience with AI safety and responsible AI practices
• LangChain, Semantic Kernel, or similar frameworks
• GitHub Copilot enterprise deployment experience