AI Engineer
Role Overview
We are seeking an experienced AI Engineer to design, develop, deploy, and maintain AI and machine learning solutions that deliver measurable business value. The ideal candidate will combine strong software engineering skills with practical experience in machine learning, generative AI, data, and cloud technologies.
The AI Engineer will work closely with data scientists, data engineers, architects, software engineers, and business stakeholders to translate business requirements into scalable, reliable, and production-ready AI solutions.
Key Responsibilities
- Design, develop, and deploy AI and machine learning solutions for business and operational use cases.
- Develop production-grade AI applications using Python and modern AI/ML frameworks.
- Build and integrate machine learning models into enterprise applications and data platforms.
- Develop solutions using Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG) where appropriate.
- Design and implement AI-powered applications, services, APIs, and automation workflows.
- Integrate foundation models and AI services into enterprise technology environments.
- Develop and optimise prompt engineering, model orchestration, and evaluation approaches.
- Design and implement RAG architectures using enterprise data, vector databases, embeddings, and knowledge bases.
- Build data preparation, feature engineering, model training, and inference pipelines.
- Evaluate models and AI solutions for accuracy, reliability, performance, cost, and business effectiveness.
- Implement monitoring, observability, testing, and continuous improvement for AI systems.
- Apply responsible AI principles covering security, privacy, bias, explainability, and appropriate use of AI.
- Work with data engineering teams to ensure AI solutions have reliable and appropriately governed data.
- Collaborate with cloud and platform engineering teams to deploy scalable AI workloads.
- Implement CI/CD and MLOps practices to support repeatable and reliable model deployment.
- Troubleshoot model, application, data, performance, and integration issues.
- Keep up to date with developments in AI, machine