AI Lead - Banking (ID 1229)

■ 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).  Strong programming discipline and production deployment best practices.  Team scaling & mentoring; effective cross-functional leadership.

 Business outcome–driven product strategy and prioritization.