onsite

Head of AI Platform Engineering and Product Stack

The Head of AI Platform Engineering and Product Stack will lead the strategic evolution of India's leading BFSI AI platform, managing a 40-person team to deliver agentic capabilities and scalable AI/ML infrastructure. This role involves defining technology strategy, architecting multi-agent solutions, and fostering innovation in AI engineering and data science to achieve business goals.

About the role

Position Summary

Lead the strategic evolution of India's most sophisticated BFSI AI platform ecosystem, directing a 40-person engineering and data science team to deliver agentic platform capabilities through cutting-edge agentic tech stack where agent creation as well agent orchestration, AI native vertical focused workflows and journeys along with scalable AI/ML infrastructure.

Qualifications & Experience

Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related from a globally ranked institution.
12+ years driving large-scale enterprise apps, including 8+ years building enterprise AI platforms and delivering multi-product rollouts in a BFSI/fintech domain.

Skills

Hands-on experience scaling AI/ML applications (e.g., Uvicorn, vLLM) in production.
Advanced orchestration of large ML systems and agentic workflows end-to-end.
Evaluation frameworks across classical ML and GenAI (task metrics, robustness, safety).
Deep infrastructure understanding (GPU/CPU architecture, memory/throughput) and MLOps for model operationalization.
Application architecture expertise: modular design, shared large-model services across multiple application components.
Modern cloud proficiency: AWS, GCP (compute, networking, storage, security).
Strong programming discipline and production deployment best practices.
Team scaling & mentoring; effective cross-functional leadership.
Business outcome–driven product strategy and prioritization.

Responsibilities

Define and lead AI platform technology strategy, driving innovation across agentic, low-code and document science platforms, advanced LLM search, and next-gen financial products.
Architect multi-agent, autonomous workflow solutions and ensure scalable, resilient ML infrastructure to support cross-domain product delivery.
Create and own the technology roadmap aligned to strategic business goals and competitive market positioning.
Lead and scale the AI engineering and Data Science team from 40+, building organizational excellence in MLEs, MLOps, and data engineering.
Establish and champion best practices in AI governance, ethical frameworks, and business impact measurement.
Drive cross-functional stakeholder engagement, collaborating closely with product, design, data, and industry partners to accelerate platform innovation and industry leadership.
Represent the company as an authority on AI within industry forums, publications, and speaking events.
Foster a culture of continuous learning, mentorship, and innovation, developing high-potential AI talent for next-generation leadership.
Own and report platform success metrics, business impact KPIs, and deliver on ambitious product growth.
Example technical challenges: Design scalable Document AI and agentic search workflows for high-volume BFSI use cases; deploy autonomous ML systems supporting real-time lending and regulatory compliance; orchestrate and optimize multi-agent workflows for financial products lifecycle.

About the role

Position Summary

Qualifications & Experience

Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related from a globally ranked institution.
12+ years driving large-scale enterprise apps, including 8+ years building enterprise AI platforms and delivering multi-product rollouts in a BFSI/fintech domain.

Skills

Hands-on experience scaling AI/ML applications (e.g., Uvicorn, vLLM) in production.
Advanced orchestration of large ML systems and agentic workflows end-to-end.
Evaluation frameworks across classical ML and GenAI (task metrics, robustness, safety).
Deep infrastructure understanding (GPU/CPU architecture, memory/throughput) and MLOps for model operationalization.
Application architecture expertise: modular design, shared large-model services across multiple application components.
Modern cloud proficiency: AWS, GCP (compute, networking, storage, security).
Strong programming discipline and production deployment best practices.
Team scaling & mentoring; effective cross-functional leadership.
Business outcome–driven product strategy and prioritization.

Responsibilities

Define and lead AI platform technology strategy, driving innovation across agentic, low-code and document science platforms, advanced LLM search, and next-gen financial products.
Architect multi-agent, autonomous workflow solutions and ensure scalable, resilient ML infrastructure to support cross-domain product delivery.
Create and own the technology roadmap aligned to strategic business goals and competitive market positioning.
Lead and scale the AI engineering and Data Science team from 40+, building organizational excellence in MLEs, MLOps, and data engineering.
Establish and champion best practices in AI governance, ethical frameworks, and business impact measurement.
Drive cross-functional stakeholder engagement, collaborating closely with product, design, data, and industry partners to accelerate platform innovation and industry leadership.
Represent the company as an authority on AI within industry forums, publications, and speaking events.
Foster a culture of continuous learning, mentorship, and innovation, developing high-potential AI talent for next-generation leadership.
Own and report platform success metrics, business impact KPIs, and deliver on ambitious product growth.
Example technical challenges: Design scalable Document AI and agentic search workflows for high-volume BFSI use cases; deploy autonomous ML systems supporting real-time lending and regulatory compliance; orchestrate and optimize multi-agent workflows for financial products lifecycle.