eTeam
Artificial Intelligence Engineer
London
Posted about 19 hours ago
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Job Title: GenAI Engineer
Location: London, UK
Job Type: Full-time contract, 6 - 12 months
Job Type: Hybrid
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.
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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.
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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.
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