About the Role
Role details below.
Responsibilities
- Crafting excellent products! As an AI Engineer, you will be building new AI-powered features that meet the needs of our clients' customers, as well as improving their Digital Platform with intelligent capabilities
- Continuously delivering changes to products including AI model integrations, prompt optimisations, and system improvements, as we push for a full CI/CD model
- Working closely with your team, regularly collaborating on both traditional engineering and AI initiatives, to continually push yourselves to build better, smarter systems
- Participating in regular show and tells to promote your work — from AI experiments to production deployments — to both your department and the wider company
- Joining our clients’ internal events, including meeting external speakers, going to quiz nights, etc.
Requirements
- Proven strong experience with JavaScript, TypeScript, and Python
- Advanced GenAI/LLM expertise: RAG, embeddings, vector search, prompt engineering, evals, guardrails, caching, chaining, function calling, etc
- Fine-tuning expertise: LoRA/QLoRA, instruction tuning, RLHF, domain adaptation, and custom model training
- Agentic architecture design: multi-agent systems, tool orchestration, planning/reasoning frameworks, memory systems, and agent coordination patterns
- Eval systems: automated evals, human feedback loops, benchmarking, A/B testing, safety testing, hallucination detection, and performance metrics
- Experience with LLM frameworks (LangChain, LlamaIndex, LiteLLM, CrewAI, Mastra, etc) and API integrations (OpenAI, Anthropic, etc.)
- CI/CD pipelines and IaC tools (Terraform, CloudFormation, CDK)
- Cloud platforms (AWS, Azure, GCP) for GenAI workloads and API management
- Containerisation and orchestration (Docker, Kubernetes) for AI applications
- LLM hosting and inference optimisation (vLLM, TGI, local deployment)
- Vector databases (Pinecone, Weaviate, Chroma, Qdrant) and traditional databases (PostgreSQL, Neo4j)
- Data pipeline design for RAG and knowledge ingestion
- Real-time data streaming and processing
- Document processing and embedding generation workflows
- React or modern UI frameworks
- API design for LLM services and conversational interfaces
- Multiple protocols (HTTP, GraphQL, gRPC, WebSockets, MCP)
- Real-time chat and streaming response handling
- Building high-performance, scalable AI applications
- LLM cost optimisation, token management, and caching strategies
- Performance monitoring, observability, and debugging production AI systems
- Comprehensive testing: unit, integration, AI behaviour, security, accessibility
Work Arrangement
Remote (Worldwide)
