AI Engineer

About the Role:

This role sits at the intersection of business, analytics, and applied AI. You'll diagnose business problems using data, and design and ship agentic workflows that solve them at scale.

What You'll Do:

▪ Design and ship LLM-powered workflows and agentic applications that solve real operational and analytical problems

▪ Architect multi-step AI systems end-to-end, including API integrations, workflow orchestration, and production deployment

▪ Write production-grade SQL and Python to extract, transform, and analyze data in support of business decisions

▪ Translate analytical findings into clear visual narratives for business stakeholders

▪ Proactively identify opportunities to automate manual or analytical workflows using AI and code

What we are looking for:

1. 2+ years of hands-on experience in analytics and 1+ years in LLM/AI workflows

2. Proficient in SQL and Python

3. Hands-on experience building LLM-powered applications, agentic workflows, or AI pipelines

4. Solid foundation in math and statistics

5. B.Tech from a reputed engineering college preferred

Here's a sneak peek at the projects:

• Creative OS – An always-on agentic loop across the influencer and ad lifecycle: from social and content extraction (creator signals, hooks, UGC trends) through to live Meta Ads performance (creative RCA, fatigue detection, and automated budget, bid and pacing decisions).

• Consumer Research – Simulation and trend engines that gauge demand before spend: mining search and social signals to rank BPC macro and micro-trends, and stress-testing claims, pricing and naming against simulated consumer cohorts so only ideas with predicted lift get scaled.

• Business Data MCP – A role-aware, natural-language layer over all of Honasa's data that lets any employee ask a plain-English question and get the right chart, cut and comparison with citations.

• Market Science – Models that define pricing and growth strategy: marketing mix modeling, revenue growth management and geo-lift experiments to set media optimization, discount elasticity and efficiency levers grounded in measured causal evidence.

• Enterprise OS – Agentic automations across finance, supply chain, Commercial ops, sales that remove manual, repetitive process work end-to-end.

• And more – Emerging applications across the value chain like formulation generation, content generation, Voice AI for sales and support, and other frontier use cases as the agent stack expands.