Mindsprint - Agentic AI Architect

Job Summary : You will lead Mindsprint's GenAI and agentic AI architecture, taking agent-based solutions from opportunity to production. You will design systems that combine LLMs, RAG, knowledge graphs, tool use through MCP and enterprise integrations, with governance, security and reliability built in.Responsibilities : - Work with business leaders to find high-value AI use cases and shape production-grade solution strategies and adoption roadmaps.- Design agentic AI architectures : single- and multi-agent orchestration, planning, memory, tool calling and human-in-the-loop controls.- Architect RAG and knowledge systems : document ingestion and parsing, chunking, embeddings, vector and hybrid search, and knowledge graphs.- Define how agents connect to enterprise systems and data through MCP servers, APIs and event-driven integration.- Select and evaluate models (OpenAI, Anthropic, open-source via Hugging Face), including fine-tuning where it pays off.- Establish enterprise AI governance : guardrails, evaluation frameworks, observability, cost controls, security and responsible-AI policies.- Build reusable AI frameworks, accelerators and reference architectures that cut delivery effort across engagements.- Lead technical workshops, demos, PoCs and executive presentations; translate AI concepts for technical and non-technical audiences.- Partner with the Data Engineering Architect to make sure data platforms are AI-ready.- Guide and mentor AI engineers, data scientists and solution architects.Must-have Skills : - 15+ years in software, data or solution architecture, with 2+ years designing GenAI or agentic systems that reached production.- Hands-on with LLM application frameworks : LangChain, LangGraph, LlamaIndex, Semantic Kernel or similar.- Strong RAG design experience with vector databases (Azure AI Search, pgvector, Pinecone, Weaviate or similar).- Working knowledge of MCP and tool/function-calling patterns.- Solid Python and API design; cloud AI services on Azure (Azure OpenAI, AI Foundry) or AWS/GCP equivalents.- Experience with LLM evaluation, guardrails and LLMOps.- Strong data architecture background : you understand how data quality and pipelines shape AI outcomes.- Proven executive-facing consulting and pre-sales experience.Good to have : - Knowledge graphs (Neo4j, JanusGraph) and semantic search (Solr, Elasticsearch).- Document AI tooling (Apache Tika, GROBID, Azure Document Intelligence).- Fine-tuning and serving open-source models.- Multi-agent frameworks (AutoGen, CrewAI, Agent SDKs).- Domain experience in supply chain, agri/food, retail, insurance or healthcare.- Published accelerators, patents, talks or innovation awards. (ref:hirist.tech)