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Chief Architect, AI-Driven Solutions (India)

SA GLOBAL ADVISORS·Posted 1 week ago

Location

About the Role

Global Chief Technology Officer (CTO) - AI-led Digital Transformation

About Client

• Our client is a pure-play Consulting and AI-led Digital Transformation leader with a global presence across 20+ cities across the US, EMEA, India and APAC regions with ~2,000 global employees.

• Ambitious vision to create one of the most unique AI-led Consulting companies on the globe through a significant focus on growing the Core Digital Transformation portfolio through large deals and deep AI Consulting, Solutions, Business Transformation, Data, Cloud and CX capabilities.

• SA is advising the client on a significant acceleration of AI-led growth through strategic investments and partnerships and expansion of Leadership to steer a 3-year roadmap to accelerate global revenues from ~$100M to $250M.

Position: Global Chief Technology Officer (CTO)

Location: Pune

Compensation: Very Competitive (Top 10 percentile in AI-led Digital Transformation industry)

What We’re Looking For:

Our client is looking for a seasoned, ambitious and results-driven CTO and Thought Leader with a proven track record of architecting AI-led Technology and Business Transformation roadmaps for Global Enterprise Clients of a multi-country AI-led Transformation businesses with $100M scale.

As the CTO, you won’t just be managing a department;
you will be the primaryarchitect of our technical evolution and the institutionalization of AI across Global practices, delivery, client engagement, and ways of working. In a landscape where “AI” is often a buzzword, your mission is to transform it into a tangible, high-margin service offering. You will bridge the gap between cutting-edge research—specifically in Agentic AI—and the pragmatic needs of our global clients. This role goes beyond technology leadership. It is about driving organization-wide adoption, experimentation, and measurable impact of AI in every client-facing process.

To lead the technical vision, strategy, and execution for a leading AI-led Digital Transformation firm. You will ensure we are not just following trends, but actively adapting emerging tech into scalable Digital Platforms, Advanced Analytics, and Autonomous Agent frameworks—with a transparent mandate to embed AI into everyday delivery, not as an initiative but as a default operating model.

Key Responsibilities:

Technical Strategy & Innovation

• Agentic AI Pioneer: Drive the shift from “Chatbots” to “Agents.” Architect frameworks for multi-agent systems (MAS) that can reason, use tools, and execute complex workflows autonomously.

• Hybrid AI Integration: Balance the deployment of traditional Machine Learning (predictive analytics) with modern Generative AI and LLM orchestration.

• Tech Stack Ownership: Standardize our tech stack across Cloud (AWS/Azure/GCP), Data Engineering, and Modern Web/Mobile platforms to ensure rapid, repeatable delivery.

• AI Dissemination & Adoption: Drive structured dissemination of AI knowledge across the organization. Establish experimentation frameworks, internal sandboxes, and rapid prototyping cycles to ensure continuous adoption of AI in delivery teams.

Delivery Excellence & Architecture

• Hands-on Leadership: While you are a strategist, you aren’t afraid to dive into code or review an architecture diagram for a critical client project.

• Scalable Platforms: Oversee the development of proprietary accelerators and digital platforms that allow us to deliver AI and Analytics solutions faster than competitors.

• Ensure these platforms are actively consumed across projects and become integral to delivery execution, not optional accelerators.

• Quality & Security: Implement rigorous standards for AI ethics, data privacy (GDPR/SOC2), and code quality.

Growth & Client Advisory

• The Technical Face: Act as a high-level consultant for key clients, helping them navigate the “Build vs. Buy” dilemma in the AI era.

• Champion AI-led transformation by embedding AI into solution design, delivery execution, and measurable business outcomes.

• Pre-sales Catalyst: Partner with the sales team to design technical solutions for proposals and participate in high-stakes pitches.

• Talent Magnet: Build and mentor a high-performance engineering culture.

• Drive a culture of continuous learning, experimentation, and applied AI thinking across all delivery teams.

Qualifications and Experience:

• 15 years in software engineering, with at least 3 years in a leadership role within an IT Services/Consulting environment.

• Degree in Computer Science, AI, or a related field (Masters/PhD preferred but not required if you have the scars from real-world deployments).

Skills:

• AI Mastery: Deep understanding of LLMs, RAG (Retrieval-Augmented Generation), Lang Chain / Lang Graph, and deployment of autonomous agents.

• Demonstrated ability to drive organization-wide adoption of AI, beyond isolated use cases.

• Data Fluency: Expertise in modern data stacks (Snowflake, Databricks, dbt) and traditional BI/Analytics. Business Acumen: Ability to translate SOTA (State of the Art) research into ROI (Return on Investment) for a small, agile business.

Desired Characteristics:

• In a large corporation, you’d be a cog in a machine. Here, you are the engine. You have the autonomy to pick the tools, define the methodology, and directly see the impact of your decisions on the company’s growth and the success of our clients.

• We are not looking for a “business as usual” CTO. We need a “builder” who is obsessed with the potential to transform businesses with AI in the next 12-to-18-month horizon—and a collaborative, force-multiplier who can embed AI vision and execution into the DNA of the organization.

HireIQ AI InsightsBeta

Ideal Candidate

A hands-on technologist who has scaled AI delivery across enterprise consulting or product companies (not pure research), with demonstrated success shipping agentic AI or LLM-powered systems into production for clients.

Estimated Salary Range(medium confidence)

₹35 L – ₹65 L per year

Likely Interview Questions

1.Walk us through a multi-agent system you've architected in production. How did you handle coordination, failure modes, and cost optimization across agent calls?
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