Generative AI Engineer
First seen by Alion on Sep 9, 2026.
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.
Skills
Generative AI, Artificial Intelligence, LLM, Copilot, OpenAI, Agentic AI, RAG, VectorDB, Python, LangChain, LangGraph