•   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.
  

Requirements

■ 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.