AI Engineer

We are seeking the expertise of an AI Engineer to build the intelligence layer of our client's platform. Systems/processes are recruitment practices, CV's, Documents, power intelligent automation within the healthcare industry.

• Build and operate Python AI/ML services that score and match workers to jobs and surface results into the product.
• Design and implement best practices for production AI: evaluation harnesses, guardrails, monitoring, reproducibility and cost control.
• Design and improve retrieval and matching pipelines: embeddings, vector search and taxonomy-driven scoring.
• Integrate large language models for extraction, summarisation, classification and intelligent automation (use AWS Bedrock today, but you should be comfortable choosing and switching providers).
• Build and train our own in-house models where they outperform or reduce reliance on third-party APIs: data preparation, training and fine-tuning, evaluation and deployment.
• Own data quality for AI features, for example ensuring embedding and taxonomy data is correct so scores are meaningful rather than degenerate.
• Partner with full stack engineers to expose AI capabilities through clean APIs, and with platform engineers on deployment and cost.
• Monitor accuracy, latency and cost in production, and iterate based on real outcomes.

• 3 to 6 years building software, with strong, production-grade Python.
• Hands-on experience building and shipping ML, NLP or LLM-powered features, not only research or notebooks.
• The ability to design and implement best practices for AI systems: evaluation, guardrails, monitoring, reproducibility and cost control.
• Practical understanding of embeddings, semantic / vector search and retrieval-augmented generation (RAG).
• Experience integrating LLMs from any provider (for example Anthropic, OpenAI, AWS Bedrock or open models) into real applications.
• The ability to build, train and fine-tune in-house models, not only consume third-party APIs: dataset preparation, training, evaluation and serving.
• Comfortable building APIs and working with relational data (FastAPI and PostgreSQL; similar tools transfer well).
• Solid grasp of prompt design, evaluation, and the failure modes of LLM and scoring systems, and able to reason about why a model behaves the way it does in production.

Nice to have

• Familiarity with managed LLM platforms (for example AWS Bedrock), including model access, secure cross-account access and cost management.
• Deep experience with model training frameworks (PyTorch, TensorFlow or Hugging Face) and serving custom models at scale.
• Familiarity with vector databases / pgvector and building taxonomy or ontology-driven scoring.
• Experience designing agentic systems, tool-use, or multi-step LLM workflows.
• Workflow orchestration / automation tooling (for example Airflow, Prefect or similar).
• MLOps experience: model/version tracking, evaluation harnesses, monitoring and observability for AI.
• Comfort with containers, Kubernetes, and deploying Python services in production.
• Background in matching, search, ranking, or recommendation systems.
• Domain experience in healthcare, recruitment or HR-tech. 

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