SNI is serving as a trusted IT Outsourcing partner in line with the needs of World's most prestigious firms and carried out successful projects worldwide.

Scope:

Design and implement end-to-end Generative AI solutions using Python
Build agentic AI systems and workflows using frameworks such as LangGraph, tool/function calling, MCP, and multi-agent orchestration
Develop advanced RAG pipelines (ingestion, chunking, embeddings, vector search, re-ranking, grounding validation)
Implement memory architectures (short-term, long-term, summarised, and vector-based memory)
Integrate, evaluate, and optimize multiple LLMs (e.g., GPT-4.x, GPT-4o)
Build structured output and tool-calling systems with schema validation
Design evaluation frameworks for hallucination detection, quality scoring, latency, and cost efficiency
Implement Responsible AI guardrails, including input/output filtering and fallback mechanisms
Collaborate with DevOps / LLMOps teams for deployment, monitoring, scaling, and cost optimization

Skills:

Strong hands-on Python development (mandatory)
Experience with GenAI frameworks such as LangChain, LangGraph, and CrewAI
Deep understanding of agentic AI systems, orchestration, and tool integration
Experience with Azure AI Foundry and Azure OpenAI Service
Familiarity with vector databases (Azure AI Search, Pinecone, Weaviate, OpenSearch)
Understanding of tokenization, context windows, rate limits, and LLM cost optimization
Experience with API integration, asynchronous programming, and resilient system design
Strong knowledge of Responsible AI principles and risk mitigation techniques

LLM Engineer

SNI