Info Edge names Jatin Thukral Chief AI Scientist and Naukri Chief Product Officer
Info Edge (India) Limited has promoted Jatin Thukral to a dual mandate: Chief AI Scientist for the parent company and Chief Product Officer for Naukri. The board approved the reorganisation via circular resolution on October 30, 2025, with the changes effective November 3. The shift signals a tighter link between AI research and product execution across the company's recruitment businesses.
Thukral has led the AI Lab at Naukri since joining in 2020, along with data science for 99acres, Jeevansathi, and Shiksha. He holds a PhD from ETH Zurich and is a CFA charterholder, with prior roles at BlackRock, Goldman Sachs, and Flipkart (data-science research). This mix of deep technical work and business context is rare-and useful-when you're turning models into products at scale.
Why this move matters for product teams
Info Edge has leaned into an AI-first approach, reporting a 15-20% lift in key metrics from enterprise-wide AI adoption in FY25. Initiatives span search and personalisation, new AI-led products like Naukri 360 and AI-Rex, and analytics. The company cites a 130-member AI team and plans to expand generative AI and automation across its portfolio. Learn more about the company's portfolio on the Info Edge website and its flagship platform at Naukri.com.
For product leaders, combining Chief AI Scientist and CPO creates one accountable owner from research to roadmap to revenue. It shortens the loop between model development, user feedback, and monetisation.
- Org design: Unifying AI and product reduces handoffs. Consider a central AI platform team with embedded product squads for each business line.
- Roadmap focus: Prioritise AI where it compounds: search relevance, matching, ranking, fraud/spam detection, and workflow co-pilots (screening, outreach, analytics).
- Experimentation: Treat models as hypotheses. Ship small, run A/Bs, and watch metric deltas at the feature level-not just top-line.
- Data quality: Invest in labeling pipelines, feedback loops, and feature stores. Good data beats a bigger model that's trained on noise.
- Guardrails: Build for reliability and safety: human-in-the-loop review, bias checks, audit trails, and clear failure modes.
- Unit economics: Track inference cost, latency budgets, and cache hit rates. Push heavy compute to batch; keep user flows snappy.
- Monetisation: Package premium features (better matches, intelligent screening, market insights) around clear value metrics-quality, speed, and outcomes.
- Platform leverage: Standardise services (auth, profiles, scoring, messaging) so wins in one brand lift the others.
Who's doing what after the restructure
Nimish Kulshrestha becomes Chief Business Officer for IIMJobs, Hirist, and NaukriGulf. Shail Gaurav becomes Chief Business Officer for Naukri 360 and Head of B2C Marketing. Atul Kumar will no longer be classified as senior management personnel after the restructure.
What to watch next
- Shipping cadence and adoption for Naukri 360 and AI-Rex.
- Search and match quality improvements measured by fill rates, time-to-hire, and candidate satisfaction.
- Clear pricing and packaging for AI-assisted workflows on the employer and candidate sides.
- Reliability and speed across the AI stack (latency, fallbacks, observability).
- Responsible AI disclosures and ongoing evaluation practices.
If you're leading product, this is a good moment to audit your roadmap: pick 2-3 workflows where AI can remove friction now, define success metrics, and ship a live experiment in 90 days. Keep the loop tight-data in, models improved, user value out.
Building AI fluency across your product org? Explore practical courses for product roles at Complete AI Training.
Your membership also unlocks:
