Head of AI Platform Engineering and Product Stack (ID 1129)
Marketscope India
Market Research · 11-50 employees
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About the role
Define and lead the AI platform technology strategy while architecting scalable, multi-agent autonomous workflow solutions. Lead and scale a large engineering team while championing AI governance and cross-functional innovation.
What they look for
Requirements
Requires deep expertise in AI/ML infrastructure, GPU/CPU architecture, and production deployment of large-scale models. Candidates must demonstrate strong leadership experience in scaling teams and driving business outcomes within complex technical environments.
Full description
- 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.
