Forward Deployment Engineer

Excerpt from the original posting

Location: India (100% Remote / Flexible)
Contract Type: Full-Time / Enterprise Project Engagement
About Xenon7 
Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.
Job Summary 
We are seeking a high-caliber Forward Deployment Engineer (FDE) to sit at the intersection of enterprise AI platforms and executive Finance business functions for our Fortune 100 enterprise client. This role bridges advanced AI platform engineering with direct business impact—translating ambiguous financial challenges into working AI applications, deploying them into regulated client environments, and iterating in tight loops to drive measurable ROI.
Rather than building static models or waiting for formal spec sheets, you will operate as a product-minded technical lead. You will prototype user-facing tools in days, deploy agentic and RAG workflows on top of enterprise data stacks (Databricks Genie), and ensure high adoption across FP&A, Controllership, Treasury, and CFO leadership.
Key Responsibilities 
End-to-End AI Application Prototyping & Delivery 
- Own the full lifecycle of AI solutions for Finance business units—from initial discovery with CFO stakeholders to production deployment, user adoption, and ROI measurement.
- Rapidly prototype vertical applications using Streamlit, Databricks Apps, FastAPI/Flask, or lightweight React interfaces within 1–2 weeks to gather real user feedback.
- Handle "last mile" execution: edge cases, data quirks, business rule exceptions, and user training to turn prototypes into sticky enterprise products.
Agentic Systems & LLM Engineering 
- Build production-grade vertical AI tools leveraging Databricks Genie (Genie Spaces, semantic models).
- Construct robust RAG pipelines incorporating advanced chunking, vector databases (Pinecone, Chroma, FAISS, Azure AI Search, Cortex Search), retrieval evaluation, grounding, and citation.
- Implement LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex) and apply rigorous prompt engineering with hallucination and accuracy evaluation discipline.
Enterprise Data & Regulated Integration 
- Deploy applications inside heavily regulated enterprise environments, adhe…

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