Description

·                     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.

2. 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.

3. 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 & 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 with a strong business mindset.

• Challenger mindset with continuous learning attitude.
• Takes ownership and delivers production-quality solutions.
• Strong communication and stakeholder management skills.
• Collaborative across engineering, SAP, cloud and business teams.

Success Measures

·                     Production-ready AI solutions delivered securely and reliably.

• Measurable business value through automation and AI innovation.
• High-quality AI outputs supported by monitoring and continuous improvement.

·                     Strong adoption across business and engineering stakeholders

Key Skills

Education

Bachelor's degree