Position: AI Czar

Location: Hyderabad/ Bangalore / Chennai

Role Overview The AI Czar will lead and be accountable for the company's entire AI agenda. This is a senior leadership role that goes well beyond evaluating emerging technologies. The AI Czar will set enterprise AI policy, oversee ethical and compliant AI use, manage model risk, define vendor and partner strategy, and build internal AI capabilities.

The role owns the identification, development, and scaling of AI-driven solutions, both for internal operations and client delivery, and is measured on the business value they produce.

Key Responsibilities

Strategy & Leadership

- Define, communicate, and drive the company's AI vision, strategy, and multi-year roadmap in line with corporate goals and growth priorities.
- Act as the central coordinator for all AI initiatives across delivery, practices, sales, pre-sales, and corporate functions, eliminating duplication and ensuring efforts are focused on the highest-value use cases.
- Build and maintain a prioritised AI portfolio with clear business cases, investment levels, and expected outcomes.
- Advise the CEO and leadership team on AI opportunities, risks, and market trends, and represent the company's AI point of view with clients, partners, analysts, and industry forums.
- Shape AI-led service offerings and go-to-market propositions, and support business development and pre-sales on strategic AI pursuits.
- Build a robust AI culture across the organisation through executive sponsorship, change management, and adoption programmes.

Technical & Execution
- Provide deep technical leadership across the AI lifecycle, including use-case discovery, data readiness, model development or selection, deployment, monitoring, and retirement.
- Establish enterprise standards for MLOps/LLMOps, model lifecycle management, evaluation, and observability.
- Work with data and architecture teams to ensure enterprise data architecture, including data platforms, pipelines, quality, lineage, and access, supports AI at scale.




- Guide architectural decisions on generative AI, agentic AI, retrieval-augmented generation, classical ML, and integration with enterprise applications.
- Own end-to-end delivery of flagship AI programmes, from proof of concept through production and scale.
- Define and track KPIs that link AI investments to business performance, including revenue impact, cost savings, productivity gains, quality improvements, and client satisfaction.
- Define the AI vendor, platform, and partner strategy, including hyperscalers, model providers, and tooling vendors, as well as build-vs-buy decisions, commercial negotiations, and partner ecosystem management.
- Build internal AI capability through a Centre of Excellence, reusable assets and accelerators, and structured upskilling and certification programmes across the workforce.

Governance & Compliance
- Establish and run an enterprise-wide AI governance framework covering policies, roles and accountabilities, approval workflows, and oversight forums such as an AI Governance Council.
- Ensure AI systems are transparent, explainable, fair, and accountable, with clear standards for bias testing, human oversight, and documentation.
- Ensure compliance with applicable regulations and standards, including GDPR, the EU AI Act, India's Digital Personal Data Protection (DPDP) Act, and client-specific contractual obligations.
- Track changes in the global regulatory landscape and assess their impact on the organisation's AI initiatives.
- Establish a model risk management framework covering risk classification, validation, monitoring for drift and performance degradation, incident response, and audit readiness.
- Partner with Information Security, Legal,



and Data Protection teams to manage data privacy, intellectual property, and cybersecurity risks specific to AI, including data leakage, prompt injection, and model misuse.
- Define acceptable-use policies for AI tools across employees and client engagements and ensure they are adopted and enforced.

Qualifications & Experience

Required

- 15+ years of experience in technology leadership, including at least 7 years leading AI, machine learning, or advanced analytics programmes at enterprise scale.
- Proven track record of taking AI solutions from concept to production and delivering measurable business outcomes.
- Deep hands-on understanding of AI/ML systems, generative AI and LLMs, model lifecycle management, and enterprise data architecture.
- Demonstrated experience designing and running AI governance, risk, and compliance frameworks.
- Working knowledge of GDPR, the EU AI Act, DPDP, and related privacy and AI regulations.
- Experience managing vendor and partner ecosystems, including major cloud and AI platform providers.
- Strong executive presence, with the ability to influence C-suite stakeholders, clients, and cross-functional teams.
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.

Preferred
- Master's or PhD in AI, Machine Learning, Computer Science, or a related discipline.
- Experience in an IT services or consulting environment, including building AI practices and client-facing AI offerings.
- Experience working across offshore and onsite delivery models.
- Familiarity with frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001.
- Relevant certifications in cloud AI platforms, data privacy (e.g., CIPP), or AI governance.
- Industry thought leadership through publications, patents, speaking engagements, or standards bodies

Note:- Interested candidates can share their updated resume at [email protected] with the subject line AI Engineering Lead Agentic AI.

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