AI Engineer

Job Summary


Responsibilities

• Build and integrate LLM-powered agentic systems, including tool use, function calling, structured outputs, and multi-step reasoning workflows.
• Design RAG pipelines using embeddings, vector search, semantic retrieval, and football data interpretation.
• Build AI/ML capabilities exposed through REST APIs and backend services.
• Evaluate and improve model quality, reliability, latency, and cost.
• Work closely with Engineering and Product to take AI systems from prototype to production.
• Debug imperfect data, failure cases, and system behaviour across AI and backend workflows.
• Stay up-to-date with developments in LLMs, machine learning, and football analytics.

Requirements

• Strong Python and software engineering fundamentals.
• Strong SQL, NoSQL, data manipulation, analytical thinking, and debugging ability.
• Practical understanding of LLMs and Generative AI.
• Working knowledge of prompt engineering, embeddings, RAG, vector search, and semantic search.
• Experience with agentic AI concepts such as tool use, function calling, and multi-step workflows.
• Ability to build and consume REST APIs.
• A production mindset: maintainable systems, reliable outputs, and sensible trade-offs around latency and cost.
• Genuine interest in football and sports.

Nice to Have

• Experience with NLP, Transformers, or multimodal AI.
• LLM fine-tuning, LoRA/PEFT, structured outputs, or AI agents.
• LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Vector databases such as Pinecone or similar.
• FastAPI, Django, or Flask.
• Docker and cloud platforms such as AWS, GCP, or Azure.
• Familiarity with football analytics metrics such as xG, xA, xT, PPDA, or VAEP.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.