Role Summary
We are seeking a senior, hands-on AI engineering professional to drive applied AI and Generative AI transformation across our consulting delivery and product development organizations. This role is responsible for designing, building, and operationalizing AI- and GenAI-enabled solutions that improve productivity, quality, and scalability of enterprise services and SAP-based solutions.
This is neither a research role nor a theoretical ML position. It focuses on practical AI and GenAI implementation inside real enterprise systems, including SAP landscapes, document-heavy processes, integrations, and workflow automation.
The individual will work closely with product teams, delivery teams, and solution architects to embed AI into how we build and deliver software and services.
Key Responsibilities
Applied AI & Generative AI Solution Design
- Design and build AI- and GenAI-powered services for document processing, classification, extraction, validation, and summarization
- Implement LLM-based solutions using APIs and enterprise-safe architectures
- Build pipelines integrating AI/GenAI services into business workflows and SAP processes
- Develop and deploy microservices supporting AI inference, orchestration, and feedback loops
- Optimize models, prompts, and RAG pipelines for accuracy, reliability, and business outcomes
- Implement human-in-the-loop, exception handling, and output validation mechanisms
Enterprise Integration & Systems Engineering
- Integrate AI services with SAP systems (S/4HANA, ECC, BTP, CPI, APIs, IDocs)
- Connect AI components with document management, workflow, and content platforms
- Design scalable, secure, and compliant enterprise-grade AI architectures
- Support embeddings, vector databases, and retrieval-augmented generation (RAG) patterns
- Ensure solutions meet performance, security, and governance requirements
Product & Delivery Transformation
- Embed AI and GenAI features into internal and client-facing software platforms
- Identify high-impact AI/GenAI use cases within consulting delivery processes
- Develop reusable AI components and frameworks
- Standardize patterns for AI-enabled service delivery
- Support internal enablement and upskilling of engineering teams
AI Governance, Risk, and Reliability
- Implement guardrails for data privacy, hallucination mitigation, and auditability
- Design monitoring and evaluation frameworks for AI and LLM outputs
- Support enterprise AI compliance and responsible AI practices
- Ensure production-grade reliability, not prototype-only solutions
Required Qualifications
AI & Generative AI Engineering
- Strong experience implementing applied AI and GenAI systems in production
- Hands-on experience with LLM platforms (OpenAI, Azure OpenAI, Anthropic, or similar)
- Experience with RAG architectures, embeddings, and vector databases
- Understanding of prompt engineering, model evaluation, and output validation
- Experience deploying AI services at enterprise scale
Software & Systems Engineering
- Strong software engineering background (Python required; Java or Node.js a plus)
- Experience building APIs and microservices
- Familiarity with cloud platforms and containerized deployments
- Experience with CI/CD, monitoring, and production support
- Strong data engineering fundamentals
Enterprise & SAP Environment
- Experience integrating AI or automation into enterprise systems
- Exposure to SAP landscapes and enterprise integration patterns strongly preferred
- Understanding of document-driven business processes and automation workflows
Professional Experience
- 7+ years in software engineering, data engineering, or AI engineering roles
- Experience in enterprise or consulting product environments
- Proven ability to move from concept to production deployment
- Comfortable working across product, engineering, and delivery teams
Preferred Qualifications
- Experience with SAP BTP AI services or hyperscaler AI platforms
- Experience with OCR, document AI, and unstructured data pipelines
- Experience implementing AI in regulated or compliance-sensitive environments
- Experience building internal AI platforms or shared services
- Experience mentoring engineers in AI-enabled development practices