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.