Building a Future-Ready Graduate Talent Strategy in an AI Disrupted Hiring Market

Discover how Draup helped turn AI disruption and campus data into smarter graduate hiring.

~30000

Employees

$4.9B

Revenue

Telecom Software & Technology Services

Industry

Chesterfield, Missouri, USA

Headquarters

About the organization

The organization is a global leader in software and technology services for the communications and media industry, serving the world's largest service providers across revenue management, network operations, and digital transformation. Operating in over 85 countries with more than 30,000 employees and annual revenues exceeding $4.5 billion, it sits at the intersection of enterprise technology and industry-wide transformation. As 5G, cloud-native infrastructure, and AI reshape the communications landscape, the organization has made building a future-ready workforce — particularly at the graduate level — a strategic priority.

The Core Challenges

Entry-Level Demand Collapse
Entry-level tech hiring had declined sharply since 2022, with global job postings down nearly 63% from peak. The organization needed a structured view of which roles were most affected, and whether this reflected a cyclical dip or a structural shift.
Role Transformation Ambiguity
The workloads of Software Development Engineers, QA Engineers, and DevOps professionals were being reshaped by AI, yet the precise shift from automated to augmented to human-led tasks remained unmapped, making it difficult to calibrate graduate expectations and onboarding programs effectively.
Campus Hiring Intelligence
The organization lacked a consolidated, data-backed view of hiring volumes, placement patterns, and recruiter trends across Tier 1 and Tier 2 B-schools in India, making it difficult to prioritize campus investment and identify the best overall talent-to-cost fit by function.
Employer Category Position
The organization had limited visibility into its hiring presence relative to peers, including IT services firms, Big Tech, hyperscalers, and telecom software competitors across Tier 1 and Tier 2 campuses, making it difficult to identify strategic whitespace for early engagement.

The Solution

DevOps Engineer
PROFILE
LOCATION ANALYSIS
TALENT MARKET INSIGHTS
CAREER PATHS
DATA INTEGRATION
TECH STACK
The image is for representational purpose only.
01
Hiring Trend & Market Intelligence
Mapped entry-level tech hiring shifts across the U.S., UK, Germany, India, and France, where job postings remain 50% below 2022 peaks. India's top IT firms hit a 20-year low in graduate intake, and 53% of active roles now require AI skills.
02
Role-Level Workload Transformation
Delivered a granular workload transformation map across five early-career engineering roles, separating AI-automated tasks from AI-augmented and human-led work. With AI now generating 30–60% of routine code, the SDE role is shifting from writing code to reviewing and validating AI output.
03
Campus Hiring Landscape Mapping
Mapped placement volumes, top domains, recruiters, and hiring locations across 51 institutes. Nearly half of Tier 1 offers concentrate among the top IIMs and ISB, while leading Tier 2 institutes offer average CTCs of INR 12–18 LPA for high-volume hiring.
04
Employer Category Intelligence
Assessed campus hiring intensity across four employer categories, from IT Services (very high presence) to Hyperscalers, which made under 2% of disclosed Tier 1 MBA offers in 2024. This limited peer presence signals a clear first-mover opportunity for the organization.

Outcome

Structured Market Intelligence

The organization gained a data-backed view of where early-career tech demand is contracting, where it is growing, and how AI is reshaping the composition of graduate hiring across functions and geographies — enabling more targeted campus strategy decisions.

Role Transformation Roadmap

Detailed workload-level transformation maps for each core engineering role gave the organization a clear framework for recalibrating graduate job descriptions, onboarding programs, and early-career expectations to reflect AI-augmented delivery models.

Tiered Campus Prioritization Framework

The organization gained a data-backed view of where to direct campus investment by tier, institute, domain, and cost band — enabling a differentiated strategy that uses Tier 2 institutes for cost-efficient operational and analytics hiring and Tier 1 institutes for leadership and high-impact strategy roles.

Strategic Whitespace Identification

Employer category intelligence confirmed that telecom software peers have negligible campus presence at both Tier 1 and Tier 2 institutes — identifying a clear first-mover opportunity to build brand recognition, initiate pre-placement engagement, and develop management talent pipelines ahead of competitors.
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
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ISO
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EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

Data Trust &
Methodology
at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company.

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.

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