AI Engineer at Corona Management Systems

Job Description

• 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.

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.