Job Summary:

You will lead Mindsprint's GenAI and agentic AI architecture, taking agent-based solutions from chance 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.

:

- 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.

Profile Description:

Must-have

- 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.

📌 Agentic Ai Architect (Bengaluru)
🏢 Mindsprint
📍 Bengaluru