Q1. To begin, could you walk us through your journey across talent acquisition — spanning campus hiring, lateral and leadership hiring, and talent operations — and how these experiences have shaped your approach to building high-quality tech talent pipelines in India?

Over the past 19 years, I’ve had the opportunity to work across the full spectrum of talent acquisition — beginning with campus hiring at UST Global, then moving into lateral hiring, talent operations, strategic sourcing, and eventually leadership hiring across APAC at organisations such as Capgemini, HPE, Epsilon, Target, and Allianz Technology

This breadth shaped my philosophy in three ways:

1. A long-term, pipeline-first approach: Campus hiring taught me that today’s interns and graduates become tomorrow’s architects, managers, and engineering leaders. Building sustainable pipelines requires multi-year engagement with universities, alumni networks, and early career programs.

2. A data-driven and operationally disciplined TA engine: My talent operations experience helped me appreciate the importance of structured processes, compliance, stakeholder alignment, and metrics. At Target and Allianz, data analytics played a central role in improving hiring efficiency, NPS, and leadership hiring outcomes.

3. A leadership-hiring lens that raises the bar: Having closed Director/VP roles across Product, Engineering, Data, and Supply Chain, I’ve learned that hiring senior talent catalyses the overall talent bar. Strong leaders create strong teams — making leadership hiring a force multiplier for an organisation.

Across these stages, I internalised one core belief:

Building high-quality tech talent pipelines in India requires a blend of long-term engagement, operational excellence, and a deep understanding of evolving skills — especially in AI, cloud, data, and full-stack engineering.

 

Q2. With AI and automation reshaping recruitment workflows in 2025, how do you see TA teams balancing efficiency gains (in sourcing, screening, assessment) with the need for fairness, human judgement and maintaining a strong candidate experience?

AI and automation have undeniably transformed recruitment — especially sourcing and first-level screening — but the real value comes from balancing algorithmic efficiency with ethical, human-centred decision-making.

Three principles are critical in 2025:

1. AI for scale; humans for judgment

AI excels at pattern matching, parsing large volumes of profiles, and shortlisting.

But decisions around fit, leadership potential, and motivation still rely on experienced TA professionals.

2. Fairness through explainability

Organisations increasingly demand transparent AI models — where recruiters can interpret why a candidate was flagged in or out. Black-box systems introduce bias; explainable AI reduces it.

3. AI-enabled but human-led candidate experience

Automation speeds up communication, assessments, and scheduling.

But empathy, clarity, and personal touch — especially during senior hiring — remain human strengths.

My belief is simple: 

AI should augment recruiters, not replace them. The best TA teams will combine automation-driven speed with human-led trust, judgement, and fairness.

 

Q3. From your perspective, how are organisations evaluating “AI readiness” or “AI fluency” as a skill during hiring — especially for tech roles? What differentiates genuine capability from surface-level familiarity?

AI fluency has become a baseline expectation in many tech roles, but organisations are struggling to differentiate genuine capability from buzzword proficiency.

Here’s how mature TA functions are assessing “true” AI readiness:

1. Application over articulation

Surface-level candidates talk about AI. Real practitioners show how they used AI in shipping features, optimising pipelines, building models, or improving workflows.

2. Problem-solving orientation

Instead of asking “What model did you use?” I’ve seen teams ask:

“What business problem were you solving, and why was AI the right approach?”

3. Ability to simplify complexity

True AI practitioners can explain a model, constraint, or trade-off to non-technical stakeholders.

This is a defining characteristic.

4. Integration mindset

Genuine AI-ready candidates understand data quality, infra readiness, model lifecycle, and how AI interacts with product/engineering systems.

In short, AI fluency is not about listing tools — it’s about demonstrating practical, outcome-linked application.

 

Q4. How is demand for AI-related skills evolving across software engineering, product, data, operations, or enterprise functions? Where is the skills gap the widest?

Demand is rising across all functions, but the patterns vary:

Software Engineering

Full-stack engineers with AI integration experience (LLM APIs, model embedding, vector search) are in high demand.

Gap: engineers who understand AI infra and model lifecycle.

Product Management

Product managers today must understand data pipelines, model behaviour, and responsible AI.

Gap: PMs who can translate AI capabilities into real product value.

Data Science / Machine Learning

Demand remains highest here — especially for applied ML, MLOps, and GenAI orchestration.

Gap: Senior data scientists who can ship models to production, not just build prototypes.

Operations & Enterprise Functions

AI in HR, Finance, and Supply Chain is expanding.

Gap: Professionals who can work with AI tools and drive adoption in traditional teams.

Across all sectors, the widest skills gap is in “AI + productisation” — the ability to take a model from concept to production.

 

Q5. With skills-based hiring and hybrid/remote models rising, how are TA strategies adapting to reach talent across Tier-2/3 tech hubs? What are the biggest challenges?

Companies are realising that India’s next wave of tech talent is emerging from Tier-2 and Tier-3 cities. TA strategies are evolving in three ways:

1. Remote-friendly hiring models

Flexible location policies, satellite offices, and gig-based models are enabling access to distributed talent.

2. Skills-first sourcing

Instead of looking for college pedigree or metro presence, organisations are emphasising coding assessments, hackathons, project portfolios, and skill credentials.

3. Partnerships with local ecosystems

Collaborations with universities, tech communities, skill hubs, and state skilling programs are becoming common.

However, challenges remain:

• Inconsistent exposure to real-world engineering problems
• Lower awareness of employer brands outside metros
• Infrastructure constraints impacting remote work
• Higher dropout ratios during background checks and onboarding

To solve this, organisations need a holistic strategy — combining brand building, community engagement, and a robust onboarding ecosystem.

 

Q6. With hiring cycles becoming more selective and cost-conscious, how do you see talent operations evolving to support faster, more data-driven recruitment without compromising quality?

Talent Operations is becoming a strategic differentiator. I see three major shifts:

1. Intelligent automation across operations

Background checks, screening workflows, adjudication matrices, and document validation are becoming AI-supported — speeding up operations significantly.

2. Integrated analytics dashboards

Real-time dashboards for funnel metrics, recruiter productivity, time-to-fill, diversity ratios, and candidate NPS allow leaders to make sharper decisions.

3. Vendor consolidation and governance

Organisations are reducing vendor dependency by creating centralised talent operations hubs that balance speed with compliance.

From my experience leading TA Ops teams at Epsilon and later at Allianz and Target

I’ve seen that strong operations create consistency, predictability, and trust — something no organisation can afford to compromise.

 

Q7. If you were advising business leaders or investors exploring India’s talent landscape for the next 3–5 years, what would you highlight as the most critical areas of investment?

India will remain one of the world’s strongest tech talent ecosystems — but organisations need targeted investments:

1. AI and Data Skills Development

AI fluency will become a baseline across engineering, product, and business roles.

Investing in AI upskilling, certifications, and hands-on labs will be critical.

2. Leadership and Managerial Capability

Strong engineering and product leaders are the foundation of resilient teams.

Leadership hiring and development should be a strategic priority.

3. Talent Acquisition Technology

AI-driven sourcing tools, assessment platforms, interview intelligence, and onboarding automation will give competitive advantage.

4. Employer Branding in Non-Metro Markets

As Tier-2/3 hubs rise, companies must invest in new brand-building playbooks beyond Bangalore, Hyderabad, and Pune.

5. Robust TA Operations

Compliance, background checks, onboarding, and data discipline will define scale and speed.