EU
GenAI Engineer
eTeam UK
🇬🇧 United Kingdom
On-site
1 week ago
- Machine Learning
- AI
- SAP S/4HANA
- SAP
- Azure OpenAI
- AWS Bedrock
- RAG
- Vector Search
- OData
- ERP
- CRM
- Python
- TypeScript
- Java
- Node.js
- Copilot Studio
- Azure AI
- LangChain
- LlamaIndex
- REST API
- GraphQL
- Docker
- Kubernetes
- CI/CD
- RBAC
- OpenSearch
- Pinecone
- LangSmith
- Azure Monitor
- AWS
- CloudWatch
1 week ago
Job Type: 6-12 Months (CTH)
Location: Braham Street, London (Hybrid)
Hours: 40 Hours per Week
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.
What You Will Do
1. 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.
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 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
GenAI Engineer · eTeam UK