Job Description
Key Responsibilities –
- Design, build and ship production-ready AI applications, services, agents and internal tools.
- Integrate foundation models, ML models, RAG, vector stores and AI services into secure enterprise workflows.
- Build pipelines for prompt management, model invocation, grounding, validation and evaluation.
- Embed security, privacy, compliance, model risk and responsible AI guardrails from design to delivery.
- Build testing, evaluation, monitoring, and feedback loops for quality, performance, safety, and cost.
- Optimise latency, reliability, and cost across AI workloads.
- Collaborate with product, data, risk, security, and platform teams to clarify requirements, surface trade-offs, and align priorities for delivery.
Essential Ingredients …
- Hands-on AI engineering experience in large, complex corporate or regulated environments.
- Ability to deal effectively with technical and non-technical stakeholders at all levels.
- Tertiary qualifications in a relevant technology, engineering, computer science, data or related discipline, or equivalent practical experience.
- Strong software engineering background across backend services, APIs and production systems.
- Practical experience operationalising LLM, ML or AI services beyond prototypes or academic projects.
- Hands-on knowledge of RAG, embeddings, vector databases, orchestration and model evaluation.
- Experience with cloud, CI/CD, automated testing, observability and enterprise release practices.
- Understanding of prompt design, evaluation, safety testing and responsible AI practices.
- Ability to balance experimentation speed with engineering quality and safety.
- Comfortable working with diverse stakeholders to triage requests, communicate constraints, and prioritise work based on impact and risk.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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