About the role

We are looking for a  GenAI Engineer to design, build, and scale production-grade Generative AI solutions that drive measurable business impact across enterprise operations. In this role, you will translate complex business challenges into intelligent, LLM-powered applications — including copilots, conversational agents, document intelligence platforms, and AI-driven automation deeply integrated with core enterprise systems.

You will work hands-on with leading managed and open-source LLM platforms — including Azure OpenAI, AWS Bedrock, Anthropic Claude, and open-source models — alongside enterprise-grade design frameworks such as Microsoft Copilot Studio. A critical dimension of this role is integrating GenAI capabilities into SAP S/4HANA and adjacent enterprise applications using APIs, events, OData services, and workflow orchestration.

This is a high-impact engineering role sitting at the intersection of AI innovation and enterprise technology. You will collaborate closely with product managers, SAP functional teams, backend engineers, and cloud platform architects to deliver AI capabilities that are not just technically sound but secure, compliant, cost-efficient, and ready for enterprise-scale adoption.

What you’ll be doing

1.1    GenAI Platform Engineering
•    Design, develop, and deploy GenAI solutions using Azure OpenAI Service and AWS Bedrock, selecting appropriate foundation models (GPT-4o, Claude, Titan, Llama, Mistral) based on use-case requirements.
•    Build, configure, and publish enterprise copilots using Microsoft Copilot Studio or equivalent low-code/pro-code frameworks, integrating with M365, Teams, SharePoint, and Dataverse.
•    Architect and implement RAG (Retrieval-Augmented Generation) pipelines using vector databases (Azure AI Search, OpenSearch, Pinecone) and structured knowledge bases.
•    Develop prompt engineering strategies — including few-shot prompting, chain-of-thought, and system instruction design — to optimize LLM output quality and consistency.
1.2    Enterprise System Integration
•    Integrate GenAI capabilities with SAP S/4HANA using OData APIs, SAP Business Accelerator Hub connectors, event-driven architectures (SAP Event Mesh, BTP), and workflow triggers.
•    Build and maintain secure API layers (REST, GraphQL) connecting AI services to ERP, CRM, and operational data sources.
•    Collaborate with SAP functional and Basis teams to align AI touchpoints with existing business processes, authorization models, and data governance policies.
1.3    Evaluation, Monitoring & Responsible AI
•    Design and implement LLM evaluation frameworks covering accuracy, hallucination rates, latency, cost per token, and end-user satisfaction metrics.
•    Establish monitoring pipelines for production AI applications using tools such as Azure Monitor, AWS CloudWatch, and LangSmith.
•    Apply responsible AI practices including content filtering, guardrails (Azure Content Safety, Bedrock Guardrails), bias detection, and explainability mechanisms.
•    Manage model versioning, deprecation cycles, and prompt version control to ensure production stability.
1.4    Architecture & Collaboration
•    Contribute to GenAI architecture decisions including platform selection, cost optimization strategies, multi-region deployment, and security design.
•    Mentor junior engineers and participate in internal knowledge sharing through design reviews, documentation, and community of practice sessions.
•    Engage with product managers and business stakeholders to translate functional requirements into technically feasible AI solution designs.

Essential Skills / Experience

Required Skills & Experience
•    3+ years of software engineering experience, with at least 2 years focused on Generative AI, LLMs, or applied ML in production environments.
•    Hands-on experience with Azure OpenAI Service and AWS Bedrock — including model deployment, API integration, token management, and cost governance.
•    Practical experience building copilots or AI agents using Microsoft Copilot Studio, Azure AI Studio, LangChain, LlamaIndex, or equivalent agentic frameworks.
•    Strong understanding of RAG architecture, vector embeddings, chunking strategies, and retrieval optimization.
•    Proficiency in Python (primary) and at least one secondary language (TypeScript, Java, or Node.js).
•    Experience integrating AI services with enterprise systems — ideally SAP S/4HANA — using OData, REST APIs, or event-driven patterns.
•    Solid understanding of enterprise security requirements including OAuth 2.0, managed identities, RBAC, secret management, and data residency compliance.
•    Familiarity with cloud platforms: Microsoft Azure (preferred), AWS, or GCP for deploying and scaling AI workloads.
•    Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for AI application deployments.
•    Experience with SAP Joule and SAP AI Core. 

Desirable Skills / Experience

Preferred / Nice-to-Have Skills
•    Experience with open-source LLMs (Llama, Mistral, Phi) — fine-tuning, LoRA/PEFT adaptation, or self-hosted inference.
•    Exposure to multi-agent orchestration frameworks such as AutoGen, CrewAI, or Semantic Kernel.
•    Knowledge of SAP BTP (Business Technology Platform) services, SAP Integration Suite, or SAP AI Core.
•    Experience with observability and tracing tools for LLM applications: LangSmith, Weights & Biases, or Arize AI.
•    Familiarity with enterprise data governance standards (GDPR, SOC 2, ISO 27001) as they apply to AI solutions.
•    Background in NLP, document intelligence, or multimodal AI (vision + text).
•    Contributions to open-source AI projects or published technical writing in the GenAI domain.