Description

The AI Engineer builds and operates the agentic workflow that powers ReaderSight — the multi-agent design that harvests signals, triggers alerts, and lets buyers query titles conversationally. This role owns the Claude LLM integration and the automated weekly scoring run end to end.

Responsibilities

- Build prescriptive and optimisation engines using mathematical programming and heuristic solvers
- Design, develope and implement LLM solutions within secure enterprise infrastructure
- Engineer LLM prompts to generate plain-language business narratives and ranked recommendations
- Build automated report generation pipelines delivering structured outputs to business consumers
- Integrate ML model outputs with generative AI components for consistent and explainable end-to-end delivery
- Build rule-based trigger engines and exception management workflows
- Support A/B testing frameworks to compare AI-driven vs. baseline recommendations




- Design and implement impact analysis and counterfactual simulation capabilities

Qualifications

- Build the multi-agent workflow: Signal Harvesting Agent, Scoring Agent, Alert Agent, and Conversation Agent

- Implement Agent Bricks orchestration within the existing Databricks setting

- Integrate and tune the Claude LLM harness for sentiment, entity, and narrative extraction from messy text and video transcripts

- Build the conversational query layer that lets buyers ask plain-English questions about any title or category

- Implement threshold-triggered alert generation with per-signal evidence summaries for flagged titles

- Build the automated weekly scoring run across all titles with an on-sale date within the 90-day window

- Support the backend logic for the Summary UI (ranked list, evidence drill-down, alert dashboard)

- Participate in LLM harness and scope testing during UAT

📌 AI Engineer (Pune)
🏢 Zensar Technologies
📍 Pune