Translating AI capabilities into business value requires a technically versatile engineer who can bridge the gap between complex AI models and practical applications. This implementation-oriented full-stack role combines software engineering expertise with specialized knowledge of how to efficiently leverage data sources, LLMs, and AI agents in production environments to deliver tangible business value.
- Accelerates user adoption through intuitive interfaces and seamless system integrations
- Creates efficient data pipelines connecting LLMs to relevant utility information sources
- Enables intelligent automation through multi-agent systems for complex workflows
- Develops sustainable solutions that can evolve with business needs and technology advances
Key Responsibilities:
- Build proof-of-concept applications and production-ready interfaces for GenAI capabilities
- Connect AI services with enterprise systems (SAP, internal databases) for data exchange
- Design and implement multi-agent architectures to solve complex business processes
- Develop tool integration frameworks allowing AI to interact with utility systems
- Create robust memory and reasoning systems for contextual, multi-step AI interactions
- Implement appropriate guardrails and safety measures for AI agent systems
- Gather requirements and translate business needs into technical implementation
- Produce documentation and knowledge transfer materials for sustainable solutions
Expected Skillset:
- Full-Stack Development: Modern front-end frameworks, back-end technologies, API design
- System Integration: Experience connecting disparate systems, data orchestration, authentication
- AI Application Patterns: RAG architectures, prompt engineering, agent orchestration frameworks
- Agent Architecture: Knowledge of multi-agent systems, collaboration protocols, tool integration
- Reasoning & Memory: Understanding of chain-of-thought reasoning, context management, planning algorithms
- User Experience: Ability to design intuitive AI interfaces with appropriate feedback mechanisms
- Rapid Prototyping: Demonstrated ability to quickly build working demos and iterate based on feedback