Senior Generative AI Engineer

About the role

We are seeking a Senior GenAI Engineer to design, build, and scale production-grade Generative AI solutions that deliver measurable business outcomes across enterprise planning, consolidation, forecasting, and reporting. You will translate complex business challenges into LLM-powered applications—copilots, conversational agents, document intelligence, AI-driven automation—deeply integrated with SAP BW/4HANA, SAP BPC, SAP Analytics Cloud (SAC), Anaplan, and adjacent enterprise applications.
You will work hands-on with Azure OpenAI / AWS Bedrock / Anthropic / open-source LLMs, along with enterprise frameworks such as Microsoft Copilot Studio. A critical dimension of the role is enabling AI within finance and performance management processes, including month-end insights, variance explanation, forecasting support, reconciliation exceptions, and decision-support copilots, integrated using APIs, OData services, event-driven patterns, and workflow orchestration.
This is a high-impact engineering role at the intersection of GenAI innovation and SAP/Planning ecosystems, partnering with product owners, SAP functional teams, platform architects, and security/compliance stakeholders.

What you’ll be doing

1) GenAI Platform Engineering

• Design, develop, and deploy GenAI solutions using Azure OpenAI / AWS Bedrock; select models (GPT, Claude, Llama, etc.) based on accuracy, cost, and performance
• Build enterprise copilots/agents (Copilot Studio or similar) integrated with M365, Teams, SharePoint, Dataverse
  Implement RAG pipelines using vector DB/search tools (Azure AI Search, OpenSearch, Pinecone) with structured enterprise data
• Develop prompt strategies (system design, function calling, structured outputs) to improve reliability and reduce hallucinations

2) SAP & Planning Ecosystem Integration

• Integrate GenAI with SAP S/4HANA, BW, BPC, SAC, Anaplan via APIs, BTP services, and event-driven workflows
• Build secure API layers (REST/GraphQL) ensuring data access, governance, and compliance
• Collaborate with SAP/Basis and Finance teams to align AI use cases with business processes and controls

3) AI-Ready Data Foundations (SAP BW/4HANA)

• Design scalable data models (ADSO, ODS, Composite Providers) for AI-driven use cases
• Enable data products with metadata, lineage, and performance optimisation for analytics and copilots

4) Planning & Performance (BPC / SAC / Anaplan)

• Deliver AI use cases: forecasting, scenario modelling, variance analysis, and insights generation
• Integrate AI outputs into planning workflows, dashboards, and enterprise models
• Support BPC optimisation/migration and enhance SAC/Anaplan planning capabilities

5) Evaluation, Monitoring & Responsible AI

• Define evaluation metrics (accuracy, latency, cost, reliability) and monitor using Azure/AWS tools
• Implement guardrails, security controls, and responsible AI practices (data protection, human-in-loop)
• Manage model lifecycle, testing, and release governance

6) Architecture & Leadership

• Contribute to platform architecture, scalability, resilience, and cost optimisation
• Translate business needs into production-ready solutions
• Drive best practices, documentation, and mentor teams

Essential Skills / Experience

•    5+ years software engineering experience with 2+ years focused on GenAI/LLMs/applied ML in production.
•    Hands-on with Azure OpenAI Service and/or AWS Bedrock (deployment, API integration, token/cost governance, security).
•    Experience building copilots/agents using Microsoft Copilot Studio, Azure AI Studio, LangChain, LlamaIndex, Semantic Kernel, or equivalent.
•    Strong RAG expertise: embeddings, chunking strategies, retrieval optimization, evaluation, and grounding techniques.
•    Strong coding skills in Python (primary) plus one of TypeScript/Node.js/Java.
•    Enterprise integration experience: OData/REST APIs, event-driven patterns, workflow orchestration; experience integrating with SAP is strongly preferred.
•    Solid understanding of enterprise security: OAuth2, managed identities, RBAC, secrets management, network controls, and data residency/compliance.
•    Experience with Docker/Kubernetes and CI/CD for AI app deployments.

SAP / Planning Experience (Must Have for this Role)
Candidates must have solid exposure to at least two of the following, with preference for all four:
•    SAP BW/4HANA: modeling, data acquisition patterns, performance optimization, semantic layers
•    SAP BPC: planning/consolidation, logic patterns, automation and operational stability
•    SAP Analytics Cloud (SAC): planning models, data actions, analytics designer, operationalization
•    Anaplan: model building, performance tuning, integrations via APIs, enterprise planning workflows

Desirable Skills / Experience

•    Experience with open-source LLMs (Llama/Mistral/Phi), fine-tuning (LoRA/PEFT), or self-hosted inference.
•    Multi-agent orchestration (AutoGen, CrewAI) and tool calling patterns.
•    Exposure to SAP BTP, SAP Integration Suite, SAP Datasphere, SAP AI Core, and/or SAP Joule.
•    Advanced LLM observability tools: LangSmith, Weights & Biases, Arize, prompt/version governance tooling.
•    Knowledge of compliance standards (GDPR/SOC2/ISO27001) applied to AI solutions.
•    NLP/document intelligence/multimodal AI experience (text + vision).
•    Contributions to open-source or published writing in GenAI.