Gen-AI 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. 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, authorizations, and data governance needs. 3. Engineer for Scale, Quality and Governance • Contribute to solution architecture, platform selection, cost optimization, 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 optimization. • 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.