Artificial Intelligence Engineer

Role Summary

We are looking for a GenAI Engineer to design, build and scale production-ready Generative AI solutions that solve enterprise business problems.

The role will focus on LLM-powered applications such as copilots, conversational agents, document intelligence solutions and AI-driven automation integrated with enterprise systems, including SAP S/4HANA.

The engineer will work with product, SAP, backend engineering and cloud platform teams to deliver secure, compliant, cost-efficient and reliable AI capabilities for business adoption.

Responsibilities

• Build GenAI Solutions
• Design, develop and deploy GenAI applications using Azure OpenAI, AWS Bedrock and Kiro
• Build enterprise copilots and AI agents using Microsoft Copilot Studio or similar low-code/pro-code frameworks.
• Create RAG pipelines using vector search and enterprise knowledge sources to ground AI responses.
• Apply prompt engineering techniques to improve response accuracy, consistency and usability.
• Integrate with Enterprise Systems
• Integrate GenAI capabilities with SAP S/4HANA using OData services, APIs, workflow triggers and event-driven patterns.
• Build secure API layers connecting AI services with ERP, CRM and operational systems.
• Work with SAP functional and Basis teams to align AI touchpoints with business processes, authorisations and data governance needs.
• Engineer for Scale, Quality and Governance
• Contribute to solution architecture, platform selection, cost optimisation, security and deployment decisions.
• Design evaluation approaches for LLM quality, hallucination risks, latency, cost and user satisfaction.
• Set up monitoring for production AI applications using relevant cloud and observability tools.
• Apply responsible AI practices such as content filtering, guardrails, bias checks and explainability where required.
• Maintain model, prompt and version-control discipline to support production stability.

Skills and Experience Required

• 5+ years of software engineering experience, including hands-on delivery of AI, LLM or applied ML solutions in production environments.
• Strong Python programming skills, with working knowledge of TypeScript, Java or Node.js as an advantage.
• Hands-on experience with Azure OpenAI Service, AWS Bedrock or equivalent LLM platforms.
• Practical experience building copilots, AI agents or intelligent automation using Copilot Studio, Azure AI Studio, LangChain, LlamaIndex or equivalent frameworks.
• Strong understanding of RAG design, vector embeddings, chunking strategies and retrieval optimisation.
• Experience integrating systems using REST APIs, OData, GraphQL or event-driven architectures.
• Understanding of cloud deployment, Docker, Kubernetes and CI/CD pipelines for AI workloads.
• Good understanding of enterprise security patterns including OAuth 2.0, managed identities, RBAC, secret management and data residency considerations.

Preferred / Good to Have

• Experience integrating AI services with SAP S/4HANA.
• Knowledge of SAP BTP, SAP Integration Suite, SAP AI Core or SAP Joule.
• Familiarity with Azure AI Search, OpenSearch, Pinecone, LangSmith, Azure Monitor or AWS CloudWatch.
• Experience with model evaluation, guardrails and responsible AI implementation in enterprise settings.

Candidate Attributes

• Customer-focused: understands business use cases and builds solutions that solve measurable problems.
• Challenger mindset: brings new ideas, learns quickly and improves existing ways of working.
• Committed: owns delivery, follows through and supports production-quality engineering standards.
• Clear communicator: explains complex AI concepts simply to technical and business stakeholders.
• Connected collaborator: works effectively across product, SAP, platform, security and business teams.

Success Measures

• Production-ready AI solutions delivered securely and reliably.
• Measurable business value through automation, productivity or better decision support.
• High-quality AI responses supported by testing, monitoring and continuous improvement.
• Strong stakeholder adoption and collaboration across business and engineering teams.