Danish Khan — Senior Software Engineer
I’m a GenAI & Agentic AI Engineer with 4.5+ years of experience building production-grade software systems and 2+ years focused on LLM-powered applications, RAG pipelines, and multi-agent workflows. At Mphasis, I design and build stateful agentic systems using LangGraph and LangChain to automate complex workflows such as Agile backlog generation, requirement analysis, and multi-step planning. I specialize in: • Agentic AI systems (LangGraph, tool-calling, memory, reflection loops) • RAG pipelines (hybrid search, metadata filtering, vector databases) • LLM orchestration (OpenAI, Claude, Llama, AWS Bedrock) • Backend APIs for AI systems (Python, FastAPI, Docker) Some real-world impact: • Reduced irrelevant document retrieval by ~35% using hybrid RAG • Improved output consistency by ~40% using stateful memory and reflection loops • Built multi-agent systems achieving ~95% alignment with product specifications Tech I work with daily: Python, FastAPI, LangGraph, LangChain, LlamaIndex, OpenAI, Claude, Pinecone, Milvus, ChromaDB, Docker I focus on building reliable, debuggable, production-grade GenAI systems — not just demos. Currently open to roles: GenAI Engineer, Agentic AI Engineer, LLM Engineer, Applied AI Engineer
Stackforce AI infers this person is a SaaS-focused GenAI Engineer with expertise in AI orchestration and automation.
Location: Bengaluru, Karnataka, India
Experience: 4 yrs 8 mos
Skills
- Agentic Ai Systems
- Rag Pipelines
- Frontend Development
- React
- Software Development
Career Highlights
- Expert in building production-grade GenAI systems.
- Achieved 95% alignment in multi-agent systems.
- Reduced irrelevant document retrieval by 35%.
Work Experience
Newton School
Coding Mentor (1 yr 4 mos)
Mock Interviewer (2 yrs)
Mphasis
Senior Software Engineer (4 yrs 8 mos)
Associate Software Engineer (1 yr 8 mos)
Education
Full Stack web Developer at Newton School
Bachelor of Technology - BTech at SVITS