01Overview
The context: Shadowfax just closed its most profitable quarter ever. 1,358 Cr revenue (+65% YoY), a fifth straight quarter of 60%+ growth, and all-time-high PAT of 65 Cr. And AI here is in production, not in slideware: our delivery-partner copilot handles ~16,000 conversations a day with ~97% resolved without human intervention, and Vision AI catches ~40% of mismatched reverse pickups at the doorstep at ~35x lower inference cost than a frontier model.
The CFO's office runs the same way. Agentic reconciliation, LLM-assisted anomaly detection, and self-refreshing dashboards already run our revenue-assurance workflows. We are hiring the engineer who takes this system 10x further.
You will be the AI engineer inside Business Finance and Revenue Assurance: one engineer, working with AI, producing the output of a team, at public-company accuracy standards. The systems you build protect revenue across 1 Cr+ shipments a month.
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Agentic AI workflows (Claude, GPT, or equivalent) that reconcile 1 Cr+ shipments monthly
LLM-assisted anomaly detection that flags non-compliance before month close, not after
ML models for revenue-leakage and fraud detection: time-series anomaly detection, transaction matching, variance decomposition
The finance data layer: SQL/Python ETL pipelines over OMS, TMS, and billing-system extracts, validated and reconciled to source
Evaluation harnesses for every AI workflow (golden cases, regression checks) so no unverified number reaches leadership
End-to-end reconciliation automation: transaction matching, variance detection, automated settlement workflows
- Rupees recovered and leakage prevented by systems you build
- Hours of manual finance work eliminated
- Accuracy of AI outputs in production: eval pass rates, error budgets
- Speed from leadership question to verified answer
- 2-4 years building production ML or AI systems; you have shipped something AI-powered that people actually use
- Proficiency in machine learning and pattern recognition, including designing, training, and evaluating models for complex business problems; NLP for document understanding and query-based analytics
- Expert Python plus deployment (FastAPI, Docker); advanced SQL and feature engineering
- Hands-on LLM work: Claude / GPT / Gemini APIs, prompt engineering, RAG, agentic workflows or MCP
- B.Tech / M.Tech in CS, Data Science, or AI-ML
- Logistics, fintech, or high-volume transactional domain experience preferred
- Accuracy obsession: an unverified number is a defect, not a draft
- Not AI research: this is applied AI on live financial data, judged by rupees recovered and hours saved
- Not a support seat: you own systems end to end, from pipeline to production to evaluation
- Not a prompt-only role: you ship code that runs unattended
- Build agentic AI in mission-critical finance at a listed company, with real P&L; data from day one
- Direct CXO exposure: your systems feed pricing, margin, and commercial decisions at the leadership table
- A team already operating AI-first, where the path from Associate to Director has been walked in two years
: Competitive CTC, benchmarked to top quartile. Fixed plus performance variable.
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