- ■ Define 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.

- ■ 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 Banking use cases; deploy autonomous ML systems supporting real-time lending and regulatory compliance; orchestrate and optimize multi-agent workflows for financial products lifecycle.

Product Strategy & Vision

- ■ Shape the future of industry agnostic agentic AI platforms and drive and reimagine market leading AT native product offering in Banking and other chosen verticals.

- ■ Define and implement scalable product roadmaps leveraging advanced ML,

LLM, and agentic systems.





- ■ Build and deploy cross-cutting products that bridge multiple financial domains and enable high-impact business outcomes.

Requirements

Proven track record in architecting and scaling AI/ML platforms,

leading hands-on teams, and operating systems in production at scale. In the past few years, delivered end-to-end GenAI solutions with deep expertise in transformer architectures, GPU optimization, LLM data preparation, fine-tuning, evaluation, and deployment.

-  Hands-on experience scaling AI/ML applications (e.g., Uvicorn, vLLM) in production.

-  Advanced orchestration of large ML systems and agentic workflows end-toend.

-  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).

-  Solid programming discipline and production deployment best practices.

-  Team scaling & mentoring; effective cross-functional leadership.

-  Business outcome–driven product strategy and prioritization.

📌 AI Lead - Banking (Noida)
🏢 MarketScope
📍 Noida