Software Engineering Professional

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.