Role Summary
We are looking for a Senior AI Engineer with strong expertise in Prompt Engineering, Generative AI, and LLM-based solutions. The role focuses on designing agentic systems, prompt frameworks, RAG architectures, and enterprise AI integrations within the Azure ecosystem. The ideal candidate will have hands-on experience building scalable GenAI applications and optimizing LLM performance for business automation.
Key Responsibilities
- Design and implement prompt engineering strategies for LLM-based applications
- Build prompt chaining, structured prompting, and few-shot prompting workflows
- Develop agentic AI systems and multi-agent orchestration frameworks
- Implement RAG (Retrieval Augmented Generation) architectures
- Integrate LLMs with enterprise systems and APIs
- Optimize context window usage and response quality
- Build tool invocation frameworks and structured output validation
- Develop vector search and embeddings-based solutions
- Collaborate with stakeholders to implement AI-driven business automation
- Prototype AI solutions using Python and Azure AI services
Technical Requirements
- Strong understanding of LLM fundamentals
- Experience with few-shot prompting and structured prompting
- Basic to advanced prompt chaining experience
- Python knowledge for experimentation and prototyping
- Familiarity with Azure OpenAI or similar LLM platforms
- Understanding of vector search concepts
- Exposure to AI output evaluation methods
- Experience in business process automation using AI
AI & LLM Expertise
- Deep understanding of LLM behavior and limitations
- Expertise in prompt chaining and prompt optimization
- Experience with tool invocation frameworks
- Structured response validation techniques
- Context window optimization strategies
Agentic Systems
- Experience with agent orchestration frameworks
- Multi-agent collaboration models
- Agent skills modeling
- Agent registries and lifecycle management
- Agent-to-Agent (A2A) integrations
- MCP connectors or contextual integration frameworks (preferred)
- Cross-system context injection
Azure Ecosystem
- Strong experience with Azure OpenAI
- Experience with Azure Cognitive Search
- Exposure to Azure AI Foundry
- Integration with Azure Functions
- API-based AI workflow implementation
Enterprise Architecture
- Experience implementing RAG architecture
- Knowledge of embeddings and vector databases
- Strong understanding of Azure AI ecosystem
- Python proficiency for prototyping and experimentation
Preferred Skills
- Experience building enterprise GenAI applications
- AI agent frameworks (LangChain, Semantic Kernel, etc.)
- Prompt evaluation and benchmarking
- Scalable AI system design
- Cloud-based AI deployment