Key responsibilities

- Lead the AI Practice, defining the vision, roadmap, and strategy for AI adoption across the organization.
- Oversee solution architecture and delivery of AI initiatives leveraging LLMs, RAG frameworks, and enterprise data platforms.
- Provide thought leadership on emerging AI technologies, frameworks, and best practices.
- Establish frameworks, governance, and reusable components for scalable AI implementation.
- Establish AI architecture principles, standards, and governance frameworks across the organization.
- Drive development of reusable solution accelerators, reference architectures, and best practices
- Build and mentor a strong AI and data science team, fostering innovation and collaboration.
- Represent the AI Practice in client discussions, steering PoCs and solution proposals for business impact.

Architecture & Solution:

- Oversee end-to-end design and delivery of AI solutions including agentic workflows, RAG systems, LLM-powered applications, and ML models integrated into enterprise platforms.
- Guide architecture reviews, technical design validations, and PoC direction for AI initiatives.
- Ensure alignment of AI solution design with cloud-native, microservices-based, and containerized deployment models.
- Partner with enterprise data and ML teams to align AI strategy with organizational goals and data maturity.

Innovation & Technology Leadership

- Guide teams on leveraging tools such as LangChain, LlamaIndex, vector databases, and orchestration frameworks.
- Promote adoption of modern MLOps practices including lifecycle management, deployment, monitoring, and evaluation of AI models.
- Rapid innovation while ensuring enterprise-grade scalability and reliability.

Stakeholder & Business Engagement

- Collaborate with CXOs, business heads, and technology leaders to identify, prioritize, and execute AI opportunities.




- Represent the AI Practice in client engagements, supporting solution proposals, PoCs, and RFP responses.
- Translate complex AI concepts into clear strategic insights for senior stakeholders.

CoE & Organizational Contribution

- Contribute to the AI Center of Excellence by driving standardization, documentation, and knowledge sharing.
- Establish evaluation frameworks for model performance, risk, and responsible AI usage.
- Influence enterprise architecture decisions and cross-functional technology roadmaps.

Qualifications

- Education: Graduate – B.E/B.Tech - IT/CS

Requirements

- Overall 20 years of experience in IT. Proven experience in leading AI strategy, architecture, and delivery at an enterprise or practice level.
- Deep knowledge of AI solution design principles, prompt optimization, and enterprise AI deployment models.
- Exposure to at least one major cloud AI platform (AWS / Azure / GCP).
- Strong familiarity with MLOps concepts and enterprise data/AI platforms.
- Strong stakeholder management, communication, and cross-functional leadership skills.
- Demonstrated ability to translate emerging AI capabilities into actionable business outcomes.
- Strategic mindset with experience in building and scaling AI teams and practices.

Technical & Domain Knowledge

- Solid understanding of LLM orchestration tools and frameworks.
- Familiarity with vector databases and LLM APIs (OpenAI, Azure OpenAI, HuggingFace, Cohere).
- Knowledge of AI solution design, prompt optimization, and deployment models.
- Awareness of ML/DL frameworks and data engineering ecosystems.
- Understanding of cloud-native and containerized environments for AI workloads.

Leadership & Behavioral Skills

- Exceptional stakeholder management and executive communication ability.
- Strategic mindset with the ability to convert emerging AI capabilities into business impact.

Track record of guiding cross-functional teams and influencing decisions

📌 Vice President - Senior AI Leader (Mumbai)
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