Build and deploy production AI applications for enterprise Finance teams, from discovery with CFO stakeholders through prototyping, regulated deployment, adoption, and ROI measurement. Develop agentic and RAG workflows using Snowflake Cortex or Databricks Genie, LLM orchestration frameworks, vector search, and evaluation harnesses. Integrate governed enterprise data and platforms, ensure compliance and observability, and advise senior Finance leaders while creating reusable technical assets.
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, adhering strictly to RBAC, data residency, audit, and compliance constraints (SOX, GxP).
- Connect solutions to governed datasets, semantic models, and core enterprise platforms (SAP, Workday, Coupa, ERP) via secure REST APIs.
- Instrument all deployed apps with usage metrics, telemetry, and business outcome tracking from day one.
Strategic Stakeholder Advisory & Pattern Extraction
- Work directly with senior Finance leadership (CFO office, FP&A, Controllership) to translate ambiguous operational friction into crisp technical roadmaps.
- Extract reusable code components, prompt templates, evaluation harnesses, and retrieval patterns into shared enterprise assets for broader deployment.
Requirements
Experience & Mindset
- Experience: 5+ years of hands-on software development and full-stack AI application deployment.
- Product-Shaped Builder: Strong bias for action; comfortable shipping rough prototypes in Week 1 over polished spec documents in Week 4.
- Ambiguity Tolerance: Proven ability to navigate vague business problems without predefined specifications.
- Stakeholder Communication: Outstanding ability to interface directly with senior business leaders without requiring intermediary Business Analysts.
Must-Have Technical Stack
- Languages & Frameworks: Python, FastAPI/Flask, SQL, and UI frameworks (Streamlit, Databricks Apps, React).
- AI Platforms: Direct hands-on experience with Databricks Genie.
- LLM Engineering: Production experience with LangChain, LangGraph, LlamaIndex, vector search engines, and prompt evaluation harnesses.
- Cloud & DevOps: Deep working knowledge of Cloud platforms (Azure preferred, AWS/GCP acceptable), modern Git workflows, CI/CD, and basic MLOps awareness (observability, versioning via LangSmith, Datadog, etc.).
Domain Competency (Finance Focus)
- Solid foundational understanding of core Finance business processes (FP&A, order-to-cash, procure-to-pay, record-to-report, close cycles) and common financial metrics (OPEX, EBITDA, gross margin, working capital).
Nice-to-Haves & Certifications
- Prior FDE, Solutions Engineer, or Field Engineer experience at high-growth AI/Data companies (Palantir, Databricks, OpenAI, Anthropic).
- Industry background in Life Sciences, Pharma, Healthcare, or CPG enterprise Finance.
- Experience with enterprise Finance software (SAP S/4HANA, Veeva, Workday Adaptive, Oracle Financials).
- Certifications: Databricks Certified, or Azure AI Engineer Associate (AI-102).
What This Role Is NOT
- Not a pure Data Scientist or ML Researcher: You will not be training or fine-tuning foundation models from scratch; you are applying existing models to business problems.
- Not a non-coding Architect: This is a 100% hands-on builder role.
- Not a pure Backend or MLOps Engineer: You must be comfortable owning the user-facing interface and direct business interaction.
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