Skip to job description
Role profile Information Technology (IT) AI/ML/NLP/Data Science India market context
CoHire Job Description Library

Vice President Technology Job Description

The Vice President Technology leads the organization's technology vision, architecture, engineering, data, and AI/ML/NLP capabilities to deliver scalable, secure, and business-aligned solutions. The role owns the technology roadmap, AI p...

Post a Job Review Matching Candidates

Template guidance only. Confirm each requirement against the actual position before publishing.

Back to JD Library
At a glance
Experience
15-22 years of progressive technology leadership experience, including 5+ years in senior/executive
Seniority
Senior Executive / Vice President
Discipline
AI/ML/NLP/Data Science
Market
India
01

Role Overview

A concise summary of the role, its purpose and the contribution expected from the person in the position.

The Vice President Technology leads the organization's technology vision, architecture, engineering, data, and AI/ML/NLP capabilities to deliver scalable, secure, and business-aligned solutions. The role owns the technology roadmap, AI product and platform strategy, data governance, cybersecurity, compliance, and vendor ecosystem while building high-performing teams. As a senior executive, the VP Technology partners with business, product, and client stakeholders to translate strategy into measurable outcomes, modernize platforms, institutionalize responsible AI, and drive digital transformation across functions, markets, and operating models. The position carries budget accountability and enterprise-wide impact.
02

Key Responsibilities

Use these as a starting point and prioritize the outcomes, activities and responsibilities that matter for the position.

  • Define and execute enterprise technology strategy, roadmap, and investment portfolio aligned to business goals.
  • Own AI/ML/NLP vision, use-case prioritization, platform architecture, and responsible AI standards.
  • Lead architecture, engineering, data, cloud, integration, and DevOps teams across locations.
  • Establish AI product management and project governance, from discovery to scaled adoption and ROI.
  • Build data management, data quality, lineage, privacy, and AI knowledge management practices.
  • Ensure cybersecurity, model risk, AI compliance, regulatory, and ethical controls are embedded by design.
  • Partner with C-suite, business units, clients, and product leaders to translate needs into technology outcomes.
  • Manage vendor, partner, and client relationships; negotiate contracts and SLAs; assess build-vs-buy.
  • Own technology budgets, cost optimization, capacity planning, and business-case realization.
  • Modernize legacy platforms and drive cloud-native, API-first, microservices, and automation adoption.
  • Set engineering standards, SDLC, quality, observability, resilience, and release governance.
  • Recruit, develop, and retain senior technologists; build AI/ML, data, and leadership bench strength.
  • Champion change management, adoption, and digital literacy across the enterprise.
  • Track KPIs for delivery, reliability, security, AI model performance, and customer/business impact.
  • Represent technology in board/executive reviews, audits, regulatory discussions, and strategic deals.
  • Drive innovation through pilots, partnerships, academic collaborations, and responsible experimentation.
  • Ensure business continuity, disaster recovery, and third-party risk resilience.
  • Foster a culture of inclusion, accountability, continuous learning, and ethical technology use.
03

Required Experience and Qualifications

Define the experience, qualifications and capabilities needed for success in this position. Separate essential criteria from preferences.

Core requirements

  • 15-22 years in technology, with 5+ years leading large teams and senior/executive portfolios.
  • Proven ownership of enterprise technology strategy, architecture, delivery, and budgets in complex organizations.
  • Deep hands-on-to-strategic experience in AI/ML/NLP, data science, data platforms, and MLOps/LLMOps.
  • Strong cloud, API, microservices, DevOps, cybersecurity, and data governance knowledge.
  • Experience with AI governance, model risk, privacy, regulatory compliance, and responsible AI frameworks.
  • Track record of launching AI/ML products and scaling them to measurable business outcomes.
  • Strong client management, vendor negotiation, and cross-functional stakeholder influence.
  • Experience leading distributed/global teams and managing multi-million-dollar budgets.
  • Excellent executive communication, board presentation, and commercial acumen.
  • Bachelor's in CS/IT/Engineering; Master's/MBA or AI/Data Science specialization preferred.
  • Relevant certifications in cloud architecture, AI governance, security, program management, or enterprise architecture preferred.
  • Ability to balance innovation speed with security, compliance, and operational resilience.
  • Strong people leadership, talent development, and change management capability.
  • Industry exposure across BFSI, SaaS, GCC, retail, healthcare, or similar complex environments is a plus.
04

Education and Credentials

Include education, licences, certifications or other credentials when relevant to the work. Distinguish essential requirements from preferred qualifications.

Education

  • Degree in Bachelor's degree in Computer Science, Information Technology, Engineering, or related field or a related field.
  • Degree in Master's degree in Computer Science, AI/ML, Data Science, or Business Administration preferred or a related field.

Certifications and credentials

  • AWS Certified Solutions Architect - Professional certification.
  • Microsoft Certified: Azure Solutions Architect Expert certification.
  • Google Cloud Professional Cloud Architect certification.
  • Certified AI Governance Professional (AIGP) certification.
  • CISSP or CISM certification.
  • PMP or PgMP certification.
  • TOGAF certification.
  • Certified ScrumMaster or SAFe Agilist certification.
Hiring guidance: Keep criteria relevant to the position and avoid using a degree or certification as a hard filter when equivalent practical capability is acceptable.
05

Skills

Organize relevant capabilities into technical, functional and soft skills where these categories apply to the position.

Each category separates the capabilities needed for the role from additional skills that add value. These examples can be edited for the specific position and industry.

01

Technical Skills

Tools, technologies, methods or specialist knowledge relevant to the work.

MandatoryEssential
AI/ML/NLP strategy and solution architecture Enterprise architecture and technology modernization Cloud-native platforms (AWS/Azure/GCP) Data platforms, data management, and data governance MLOps/LLMOps and model lifecycle management API-first, microservices, and integration architecture Cybersecurity, privacy, and AI risk controls DevOps, CI/CD, observability, and reliability engineering Generative AI and NLP application development AI compliance and responsible AI frameworks Cross-functional Collaboration
PreferredAdds value
Generative AI/LLM orchestration and RAG architectures Vector databases and AI search platforms Kubernetes, containers, and platform engineering Snowflake, Databricks, or modern data lakehouse tools TensorFlow, PyTorch, and model evaluation tooling Responsible AI tooling and model observability RPA, low-code, and intelligent automation Edge AI and real-time inference Executive Coaching Visionary Thinking
02

Functional Skills

Capabilities used to carry out responsibilities and deliver expected outcomes.

MandatoryEssential
Technology strategy and roadmap development AI product management and AI project management AI knowledge management and data management Client management and executive stakeholder engagement IT budgeting, vendor management, and contract negotiation AI compliance management and model risk governance Digital transformation and operating model design Talent management and capability building Program and portfolio governance Business case development and ROI tracking
PreferredAdds value
FinOps and cloud cost optimization Agile/SAFe and product operating models ITIL/COBIT and technology service governance NIST AI RMF, EU AI Act, and AI assurance practices Industry 4.0 and domain-specific digital transformation M&A integration and technology due diligence Global capability center operations Regulatory audit and third-party risk management Innovation portfolio and partnership management Data monetization and AI commercialization Cybersecurity frameworks and zero-trust strategy
03

Soft Skills

Behaviours that support collaboration, communication, ownership and effective execution.

MandatoryEssential
Executive leadership and presence Strategic thinking and commercial acumen Stakeholder communication and influence Decision-making under uncertainty Coaching and mentoring senior leaders Change leadership and adoption management Ethical judgment and accountability Customer empathy Resilience and crisis management
PreferredAdds value
Board-level storytelling Influence without authority Cross-cultural leadership Conflict resolution and negotiation Customer-centric innovation Transparency and trust-building
Keep criteria realistic: Mark a skill as Mandatory only when it is genuinely essential for the role. Preferred skills should strengthen a match, not automatically disqualify a candidate.
07

Top 5 Interview Questions

Use these questions to assess practical judgement, role knowledge and applied problem-solving. Expand each one for answer signals.

1. How would you define a three-year AI/ML/NLP technology strategy for a multi-business enterprise?
Look for: Strong answers link AI use cases to business value, define platform and data architecture, prioritize a portfolio, establish responsible AI governance, plan talent and funding, set measurable KPIs, and sequence quick wins with scalable foundations.
2. Describe an AI product you scaled and how you measured ROI.
Look for: Look for a clear problem statement, data and model choices, MLOps and adoption approach, risk controls, baseline and post-launch metrics, iteration, and business impact such as revenue, cost, risk reduction, or customer experience.
3. How do you operationalize responsible AI and compliance across models and vendors?
Look for: Expect policy and risk tiering, model inventory, testing and monitoring, privacy and security controls, vendor contract clauses, audit trails, escalation paths, board reporting, and alignment to frameworks such as NIST AI RMF or EU AI Act principles.
4. How do you lead distributed engineering, data science, and platform teams through transformation?
Look for: Strong responses cover operating model design, clear ownership, talent development, change management, inclusion, communication cadence, delivery and reliability metrics, and collaboration with product and business stakeholders.
5. An enterprise AI initiative is failing after six months. What do you do?
Look for: Candidate should diagnose root causes, reassess value and feasibility, align stakeholders, re-scope or stop the initiative, strengthen governance, communicate transparently, protect critical operations, and capture lessons for the portfolio.
Interviewing guidance: Adapt follow-up questions to the candidate's experience and the actual system. Use the same core criteria consistently across candidates.
08

Career Path and Progression

Show how this role may progress over time, including adjacent paths and possible next-step roles.

Demand for VP Technology talent in India remains strong as enterprises scale digital, cloud, data, and AI/ML/NLP initiatives. Sectors such as GCCs, BFSI, SaaS, fintech, healthcare, retail, and manufacturing seek leaders who can combine enterprise technology execution with responsible AI and commercial outcomes. The role is increasingly a pathway to CTO, Chief Digital Officer, or Chief AI Officer. Candidates with proven AI product scaling, MLOps, data governance, and regulatory compliance experience command premium compensation, while generalist IT leadership roles face more competition.

Software Engineer / Data Scientist Engineering Manager / Data Science Manager Director of Engineering / Head of AI Vice President Technology Chief Technology Officer / Chief Digital Officer Chief AI Officer / Board Advisory
09

History and Background

Use this section for relevant history, origins, evolution or background related to the role, discipline, technology, business function, industry or process.

The VP Technology role emerged as technology moved from back-office support to a strategic driver of revenue, customer experience, and operating scale. Early equivalents were senior IT managers responsible for infrastructure and applications; as organizations adopted cloud, data platforms, APIs, and digital products, the role expanded into enterprise architecture, product engineering, cybersecurity, and vendor governance. In India, global capability centers, SaaS, fintech, BFSI, and digital-native businesses accelerated demand for VP Technology leaders who can run large distributed teams and partner with global stakeholders. The AI/ML/NLP era has shifted the role again: VPs now own responsible AI, data/AI platforms, MLOps, model risk, and AI product portfolios, acting as a bridge between C-suite strategy, engineering execution, and client value.
10

Salary and Compensation Context

India market benchmark for this role. Compensation varies by experience, location, organization, industry and scope.

Mean total pay
₹75.0 LPA
Indicative India market mean
Median total pay
₹65.0 LPA
India market median
Observed maximum
₹150.0 LPA
Reported market benchmark
How to read this benchmark The mean and median are indicative total-pay figures for Vice President Technology roles in India. The maximum is an observed reported value, not a guaranteed market ceiling. Actual compensation should be calibrated to the role's experience range, location, company, industry, skills and responsibilities.
Market snapshot: November 2026. Indicative compensation data compiled from verified industry sources. Benchmark methodology and sample coverage can change over time.
Common role questions

Vice President Technology FAQs

What does a Vice President Technology do?
A VP Technology leads enterprise technology strategy, architecture, engineering, data, AI/ML/NLP platforms, cybersecurity, budgets, vendor ecosystems, and teams. The role translates business goals into scalable technology outcomes and ensures responsible, compliant, and measurable digital transformation.
How is VP Technology different from a CTO?
A CTO often owns product/technology vision, innovation, and external technology positioning, while a VP Technology typically leads enterprise IT strategy, delivery, platforms, governance, and operations. In smaller firms the responsibilities can overlap significantly.
What experience and qualifications are needed for a VP Technology role?
Most VP Technology roles require 15+ years in technology, 5+ years in senior leadership, and proven experience in enterprise architecture, cloud, data, AI/ML, cybersecurity, budgets, and stakeholder management. A bachelor's in CS/IT/Engineering is common; an MBA, master's, or certifications add advantage.
What is the salary of a Vice President Technology in India?
VP Technology compensation in India varies by sector, company size, and AI expertise. Indicative LPA ranges are mean around 75, median around 65, and top roles exceeding 150 LPA, with bonuses and equity often additional.
How can someone become a VP Technology?
Build deep technology and delivery credibility, progress through engineering/data leadership roles, own large budgets and platforms, demonstrate AI/ML and governance expertise, manage senior stakeholders, and develop commercial and people leadership skills. An MBA or executive certification can help.

Ready to hire for this role?

Use this role profile as a starting point, tailor the criteria to the position, publish your requirement and review professionals with relevant skills.

Post a Job Review Matching Candidates

This role profile is a customizable template, not a live vacancy. Confirm scope, skills, experience, compensation and hiring criteria with the hiring team before publication.