AI Engineer

Role & responsibilities

• Build and ship production-ready AI/ML featuresfrom 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, re-ranking).

• 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 and online/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 ongoing maintainability.

• Conduct code reviews, write unit/integration/e2e tests (including data and prompt tests), and uphold engineering standards and documentation.

• Work with advanced AI/ML frameworks, cloud services, and container orchestration platforms.

• As an AI Engineer, you are responsible for designing, building, and deploying scalable AI and machine learning solutions that solve realworld business problems, partnering closely with data scientists to productionize models and integrate them seamlessly into applications and enterprise workflows

Preferred candidate profile