> Markdown version of [/jobs/ext/2820565-senior-ai-engineer](https://www.wearedevelopers.com/jobs/ext/2820565-senior-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - **Company:** aPriori Technologies - **Location:** Belfast, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Computer Programming, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Stores, Monitoring of Systems, Python (Programming Language), Performance Tuning, Tensorflow, Azure Machine Learning, Software Engineering, Data Streaming, Management of Software Versions, Chatbots, Pytorch, Large Language Models, Prompt Engineering, Build Management, Scikit Learn, Information Technology, Apache Kafka, Machine Learning Operations, GPT, Data Pipelines - **Published:** September 10, 2026 - **Apply:** https://www.collegerecruiter.com/job/2840589240-senior-ai-engineer ## About the Role * Hands-on familiarity with Prompt Engineering by leveraging LLM frameworks such as LangChain. * Strong programming skills in Python and familiarity with modern data/ML pipelines. * Solid understanding of data engineering practices (ETL/ELT, streaming, orchestration with Airflow/Temporal, dbt, Kafka, etc.). * Knowledge of LLMOps/MLOps practices: CI/CD for ML, model monitoring, drift detection, evaluation metrics, governance. * Strong collaboration and communication skills: able to partner with Product, Platform, and Data teams to drive AI features from concept to production. * Demonstrated ability to mentor and upskill engineers, particularly in data/ML workflows. * Skilled in designing/building/deploying/operating LLM standard model-powered features in production (chatbots, copilots, RAG systems, agents) * Proficient in working with traditional cloud AI/ML platforms such as Amazon Sagemaker, GCP Vertex AI, or Azure ML and frameworks such as TensorFlow, PyTorch, scikit-learn., * 7+ years of professional software engineering experience, including 3+ years experience in traditional AI/ML and 1+ year experience in building LLM applications on standard models. * Bachelor's or Master's in Computer Science, AI/ML, Data Science, or related field (or equivalent experience). ## Description * Design and build production-ready AI/ML systems, with an emphasis on Standard Model LLM-powered product and platform features. * Leverage LLM tooling/APIs such as LangChain and MCP connectors to implement retrieval-augmented generation (RAG), copilot-assistants and agentic workflows. * Partner with Product Management and product teams to translate requirements into AI-powered capabilities that surface directly in user-facing products. * Apply MLOps and LLMOps best practices: monitoring, evaluation, prompt versioning, cost/performance optimization. * Combine traditional AI/ML with modern GenAI approaches to deliver hybrid solutions where appropriate. * Collaborate with Data Engineers to establish a scalable data pipeline that serves structured data shaped for LLM consumption, feature store data for traditional AI, and Trad/GenAI-enhanced insights for internal and customer-facing BI use cases. * Mentor and upskill peers in core AI/ML and LLMOps practices, raising the overall AI/ML competency of the team. * Stay current with developments in GenAI, LLMOps, generative AI safety frameworks, and evaluate their potential for adoption within the platform. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Prompt Engineering is a Job of the Past](https://www.wearedevelopers.com/magazine/342-prompt-engineering-is-a-job-of-the-past) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)