Ai Engineer

Build and ship productionready AI/ML features—from data ingestion and feature engineering to model training, evaluation, and deployment. Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, reranking). Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/TPU), and efficient memory/latency tuning. Build and maintain MLOps/LLMOps workflows—CI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments. Instrument observability for data, models, and prompts (telemetry, metrics, traces, dashboards, alerts);
implement A/B tests andonline/offline evaluation. Embed Responsible AI considerations (fairness, explainability, safety, bias testing) and document assumptions, datasets, and limitations. Document architecture, workflows, and best practices to support scalability and ongoin...