AI Engineer
qfyre
Job Description
- Design, build, and deploy machine learning and generative AI models, including LLMs, embeddings, transformers, and RAG pipelines
- Develop scalable AI services and microservices using Python, REST APIs, and cloud-native technologies, optimised for performance, accuracy, and cost efficiency
- Work with structured and unstructured datasets for feature engineering, vectorisation, and model training, and build data pipelines for training, validation, and inference
- Collaborate with data engineering teams on data ingestion, storage, and governance
- Implement CI/CD pipelines for ML models (MLOps), monitor model performance and drift, and implement retraining strategies
- Manage model lifecycle, logging, and observability
- Integrate AI systems with enterprise applications, APIs, and cloud platforms (Azure, AWS, or GCP)
- Build Retrieval-Augmented Generation (RAG) architectures using vector databases such as Pinecone, FAISS, Weaviate, or Azure AI Search, aligned with enterprise security, compliance, and ethical AI standards
- Work with product, engineering, domain experts, and business teams to translate requirements into technical solutions, and communicate AI capabilities and limitations to non-technical stakeholders
- Conduct proofs of concept, demos, and conceptual solutioning