Infrastructure & Performance Optimization: Manage and optimize GPU/accelerator infrastructure (on cloud platforms like AWS, Azure, GCP or on-prem NVIDIA clusters) to support high-performance model training and inference. Drive token efficiency, latency reduction, and cost optimization in AI pipelines (through techniques like model quantization, batching, and caching).
Partner with internal departments and external stakeholders, including auditors, banks, tax advisors, and government authorities, on finance-related matters.
Own model recalibration on a scheduled cadence, maintaining documented accuracy and precision thresholds for each model before any commercial reliance is permitted.
Brainstorm new and creative growth strategies (including identification of new trends in digital marketing and evaluate new technologies) adhering to best practice guidelines
Document adoption plans, site matrices, and multi-functional communications to build detailed roadmaps, ensuring all project collaborators remain aligned at every stage of the onboarding lifecycle.
Partner with internal departments and external stakeholders, including auditors, banks, tax advisors, and government authorities, on finance-related matters.
Brainstorm new and creative growth strategies (including identification of new trends in digital marketing and evaluate new technologies) adhering to best practice guidelines
Establish and govern the lifecycle for traditional ML, Generative AI and agentic systems from ideation to production. Define frameworks policies and processes to assess, mitigate and monitor AI risks throughout the AI lifecycle.
Instrument the agent fleet with data pipelines and dashboards; apply data science techniques to understand token efficiency, failure modes, latency distribution, and business outcome correlation