Job Summary We are looking for a hands-on Senior AI Engineer to lead the design and delivery of enterprise GenAI solutions. This role will focus on building practical, scalable and secure AI-enabled applications that help organisations improve knowledge discovery, automate complex workflows, and make better use of structured and unstructured information. This is a senior engineering role requiring strong technical ownership, practical delivery experience, and the ability to work independently across complex and ambiguous problem areas. The successful candidate will be expected to take ownership of key solution components, proactively identify gaps, propose solution options, and drive implementation with minimal oversight. The role will support a range of enterprise GenAI initiatives, including intelligent assistants, advanced search and retrieval, knowledge discovery, information extraction, workflow augmentation, and AI-enabled decision support. The successful candidate should be comfortable working in enterprise environments where security, reliability, auditability and responsible AI practices are essential. The ideal candidate will have strong hands-on Python engineering experience, practical experience building LLM-powered applications, and a solid understanding of retrieval, grounding, cloud-native engineering, responsible AI and production-aligned solution design. Key Responsibilities
- Lead the design and implementation of enterprise GenAI solution components from concept through to delivery.
- Build and enhance AI application pipelines covering data ingestion, retrieval, grounding, response generation, evaluation and monitoring.
- Design effective retrieval and search patterns across structured and unstructured information sources.
- Improve answer accuracy, consistency and business relevance through strong engineering design, grounding techniques and quality controls.
- Work with architects, developers, analysts and business stakeholders to translate complex business problems into practical AI engineering solutions.
- Support secure and scalable integration of AI capabilities into enterprise technology environments.
- Apply responsible AI principles, including privacy, security, transparency, auditability, fallback behaviour and human oversight where appropriate.
- Troubleshoot complex AI application issues, including retrieval gaps, poor context selection, inconsistent outputs and response quality concerns.
- Contribute to production readiness, including performance, scalability, monitoring, maintainability and operational support.
- Help define reusable engineering patterns that can be applied across multiple enterprise AI use cases.
- Provide technical leadership and guidance to support delivery teams and improve engineering quality.
Skill Requirements
- Strong hands-on experience in Python for backend, AI, automation, data engineering or cloud-native workloads.
- Proven experience designing and delivering LLM-powered or GenAI applications in enterprise environments.
- Strong understanding of retrieval-augmented generation, embeddings, vector search, prompt grounding and response generation.
- Experience working with structured and unstructured data sources, including documents, metadata, business artefacts or operational information.
- Experience designing AI solutions that are secure, auditable, explainable and suitable for enterprise use.
- Familiarity with cloud-native application design and modern engineering practices.
- Ability to troubleshoot complex AI application quality issues and identify practical remediation options.
- Strong ownership mindset with the ability to work independently, lead problem-solving and operate effectively without detailed task-level direction.
- Ability to communicate clearly with both technical and non-technical stakeholders.
Other Requirements
- Experience with GraphRAG, relationship modelling or advanced knowledge discovery patterns.
- Experience with AI evaluation, retrieval quality assessment, confidence scoring, feedback loops or response quality tuning.
- Experience with document understanding, information extraction, intelligent search or workflow automation.
- Experience working with complex enterprise systems, technical metadata or application modernisation initiatives.
- Exposure to source-code analysis, dependency mapping, application intelligence or similar technical analysis patterns.
- Experience with data mapping, data migration support, lineage, transformation rules or metadata-driven analysis.
- Experience designing AI-enabled workflows that include review, validation, exception handling or auditability.
- Experience working in regulated or large enterprise environments.
- Familiarity with one or more major cloud platforms and common cloud-native AI architecture patterns.
- Exposure to frontend development for AI-enabled applications is beneficial but not essential.
