Job Description: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.

EXPERIENCE REQUIRED:

• 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

EXPECTED SKILLSET:

• Full-Stack Development: Modern front-end frameworks, back-end technologies, API design
• System Integration: Experience connecting disparate systems, data orchestration, and authentication
• AI Application Patterns: RAG architectures, prompt engineering, agent orchestration frameworks
• Agent Architecture: Knowledge of multi-agent systems, collaboration protocols, and tool integration
• Reasoning & Memory: Understanding of chain-of-thought reasoning, context management, and 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.
  Job Types: Full-time, Contract
  Pay: $80.00 - $90.00 per hour
  Expected hours: 40 per week

Benefits:

• Health insurance

Schedule:

• 8 hour shift
• Monday to Friday

Work Location: Remot

• 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