Lead AI platform enablement workstreams, enable upcoming AI features and products for the organization, onboarding users/teams and use cases onto enterprise AI platforms (e.g. OpenAI, Gemini Anthropic, Amazon Bedrock, LLM gateways, and agentic frameworks).
Coordinate with cross-functional stakeholders — Information Security, Risk, Compliance, Legal, and business teams — to review, negotiate, and agree on platform controls and guardrails.
Translate agreed security, risk, and compliance requirements into technical controls, implemented via platform configuration changes or custom code (e.g., IAM policies, guardrails, content filters, logging/monitoring, rate limits, data-access controls). Work with cross engineering teams to enable these controls.
Review and document controls, obtain signoffs, and maintain evidence for audit and compliance reviews.
Ensure adherence to enterprise governance, DevSecOps protocols, and responsible AI standards across the platform.
Manage token budgets, usage/credit limits, quotas, and rate limits across providers and teams; define allocation models that balance user productivity with cost discipline.
Proactively monitor AI platform costs and usage; build dashboards, anomaly detection, and automated alerting to notify users and teams of unusual spend, usage spikes, or quota breaches before they become budget issues.
User Support & Enablement
Provide day-to-day user support on credit/usage limits, quota management, and cost allocation for AI platform consumption.
Advise users on AI usage guidance, approved patterns, and platform best practices.
Support and troubleshoot technical questions related to skills, agents, MCP (Model Context Protocol) servers/integrations, prompt-based applications, and API usage.
Create and maintain runbooks, FAQs, onboarding guides, and self-service documentation to scale support.
Monitor operational metrics, usage, and incident data to drive continuous improvement, reliability, and platform adoption.
Design, build, and maintain scalable Gen AI platform capabilities, including LLM pipelines, agentic workflows, MCP integrations, and Graph/RAG architectures, using clean, maintainable Python and AWS-native tooling.
Implement cloud-native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athena.
Automate platform provisioning, control enforcement, policy checks, and cost guardrails (budgets, alerts, quota enforcement) using Infrastructure as Code.
Act as a subject matter expert (SME) on Gen AI platform technologies and help shape the organization's AI platform roadmap.
Own end-to-end delivery of platform enablement initiatives; manage timelines, deliverables, and milestones using Agile practices (Scrum/Kanban).
