Design and implement end-to-end AI solutions for document understanding and automated report generation.
Build and deploy LLM-based systems, including RAG pipelines, to retrieve and combine context from multiple data sources.
Work with unstructured and semi-structured data such as PDFs, documents, images, and historical records, transforming it into usable inputs for AI systems.
Rapidly prototype, test, and iterate on AI solutions to deliver measurable improvements.
Integrate AI outputs into existing systems via APIs, collaborating with backend teams working in C# and SQL Server environments.
Ensure solutions are scalable, reliable, and aligned with real-world business workflows.
Address performance, accuracy, and usability challenges in AI-generated outputs.
Operate within data privacy and security constraints, including handling sensitive information and exploring local model deployment where required.
Collaborate with internal teams to understand requirements and refine use cases.
Communicate technical concepts, trade-offs, and solution approaches clearly to non-technical stakeholders.
Contribute to shaping the organisation’s approach to AI adoption and best practices.

Proven experience building real-world AI applications using LLMs, particularly RAG or document-based AI systems.
Strong proficiency in Python for building data pipelines, AI workflows, and integrations.
Experience delivering end-to-end AI solutions, from concept to production-ready systems.
Hands-on experience working with unstructured data, including documents such as PDFs and mixed-format sources.
Demonstrated ability to rapidly prototype and iterate, moving quickly from idea to working solution.
Experience integrating AI systems into existing applications via APIs.
Experience working in environments with legacy systems and integrating with backend technologies.
Familiarity with cloud or hybrid environments (Azure, AWS, or similar) for deploying AI solutions.
Strong problem-solving skills and a pragmatic approach to building solutions that work in practice.
Ability to communicate effectively with both technical and non-technical stakeholders.

Understanding of data privacy and security constraints, including GDPR and handling sensitive data.
Experience working with locally hosted models or secure AI deployments.
Strong product mindset with the ability to define approaches, not just execute tasks.
Experience working in ambiguous or evolving environments.
Interest in helping shape AI capabilities and strategy within a business.
Experience working closely with cross-functional teams in a collaborative setting.

Applied AI Engineer

Company