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Senior AI Engineer (Prompt Engineering & GenAI Focus)
Senior AI Engineer (Prompt Engineering & GenAI Focus)
The Senior AI Engineer will specialize in Prompt Engineering and Generative AI, focusing on designing agentic systems, prompt frameworks, RAG architectures, and enterprise AI integrations within the Azure ecosystem. This role involves building scalable GenAI applications, optimizing LLM performance, and implementing AI-driven business automation.
About the role
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