Head of AI Platform Engineering and Product Stack (ID 1129)
Summary
Leadership role heading AI platform engineering and product: defining strategy for agentic, low-code, document-AI and LLM-search platforms serving financial (BFSI) products, architecting multi-agent ML workflows, and scaling a 40+ AI/data science team. Core stack: ML/GenAI, vLLM/Uvicorn, MLOps, AWS/GCP.
- 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.