AI Engineer

• Build and own production AI systems end-to-end, including LLM-powered applications, intelligent agents, conversational AI, document processing, decision-support tools, and automation workflows, from architecture and prototyping through deployment, monitoring, and continuous improvement.
  
• Design agent, RAG, and context-engineering systems, including retrieval, embeddings, reranking, grounding, memory, compaction, caching, token management, tool/function calling, agent harnesses, retries, state management, and failure recovery.
  
• Build robust AI evaluation and quality frameworks to measure accuracy, relevance, hallucination, safety, retrieval quality, latency, cost, and task completion, using regression testing, human evaluation, LLM-as-judge, and production feedback to continuously improve system performance.
  
• Evaluate, train, deploy, and optimise AI models, selecting between frontier and open-weight models and applying techniques such as SFT, LoRA/QLoRA, preference tuning, quantisation, and self-hosted inference where appropriate, while optimising for capability, security, privacy, latency, and cost.
  
• Engineer reliable and secure production AI infrastructure, developing Python/FastAPI services, APIs, data and inference pipelines, and observability while collaborating with Product, Software, and Platform Engineering teams and maintaining appropriate technical documentation, security controls, and responsible AI practices.
  
• 3+ years of relevant experience in AI Engineering, Machine Learning Engineering, Applied AI, Data Science, or related roles, with demonstrable experience designing, shipping, and maintaining LLM-powered systems in production.
  
• Strong proficiency in Python, FastAPI, APIs, SQL/databases, Git, Docker, and Linux, with solid software engineering fundamentals including asynchronous systems, retries, state management, testing, observability, debugging, scalability, latency, and cost optimisation.
  
• Hands-on experience with LLMs, Generative AI, RAG, agents, and context engineering, including prompt engineering, embeddings, vector/hybrid search, retrieval, reranking, tool/function calling, memory, context compaction, caching, grounding, and token management.
  
• Demonstrable experience building AI evaluation and model engineering pipelines, including regression testing, LLM-as-judge/human evaluation, hallucination mitigation, model benchmarking, and working with open-weight models through techniques such as SFT, LoRA/QLoRA, DPO/preference tuning, and production inference.
  
• Experience with relevant AI/ML and production technologies such as PyTorch, Hugging Face, vector databases, commercial and self-hosted LLMs, and inference frameworks such as vLLM/SGLang, with a strong understanding of AI security, privacy, responsible AI, model selection, performance, and production reliability.
  
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Requirements

• 3+ years of relevant experience in AI Engineering, Machine Learning Engineering, Applied AI, Data Science, or related roles, with demonstrable experience designing, shipping, and maintaining LLM-powered systems in production.
  
• Strong proficiency in Python, FastAPI, APIs, SQL/databases, Git, Docker, and Linux, with solid software engineering fundamentals including asynchronous systems, retries, state management, testing, observability, debugging, scalability, latency, and cost optimisation.
  
• Hands-on experience with LLMs, Generative AI, RAG, agents, and context engineering, including prompt engineering, embeddings, vector/hybrid search, retrieval, reranking, tool/function calling, memory, context compaction, caching, grounding, and token management.
  
• Demonstrable experience building AI evaluation and model engineering pipelines, including regression testing, LLM-as-judge/human evaluation, hallucination mitigation, model benchmarking, and working with open-weight models through techniques such as SFT, LoRA/QLoRA, DPO/preference tuning, and production inference.
  
• Experience with relevant AI/ML and production technologies such as PyTorch, Hugging Face, vector databases, commercial and self-hosted LLMs, and inference frameworks such as vLLM/SGLang, with a strong understanding of AI security, privacy, responsible AI, model selection, performance, and production reliability.
  

Benefits

• Opportunity to build and shape production AI products and AI engineering standards.
  
• Access to challenging real-world problems and opportunities to take solutions from experimentation to production.
  
• Professional development and certification support.
  
• Competitive salary and benefits.