Description

Responsibilities

• 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.
• Engineering & Delivery
• 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).

Skills

Must have

• 6+ years of progressive engineering experience, including 1-2+ years in AI platform, cloud platform, or emerging-tech enablement roles.
• Demonstrated experience working with InfoSec, Risk, and Compliance teams to define, review, and implement technical controls in regulated environments.
• Hands-on experience implementing controls through configuration and code: IAM/access policies, guardrails, logging and audit trails, quota/rate limiting, and network/data-protection controls.
• Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering techniques.
• Agentic AI, MCP, and Graph/RAG architectures, including building and supporting agents, skills, and MCP servers.
• Gen AI frameworks and LLM gateway/proxy patterns.
• AWS cloud services (AgentCore, Bedrock, EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation), including cost management and usage/credit monitoring.
• Infrastructure as Code (Terraform, Puppet, Docker) and containerized deployments.
• Python programming (NumPy, Pandas, Boto3) for automation, tooling, and platform services.
• Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4j, Neptune) and query optimization.
• Automated testing and evaluation frameworks (Ragas, Playwright, Selenium, Zephyr).
• Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align).
• Strong stakeholder management and ability to broker agreements across security, risk, compliance, and engineering teams.
• Clear written and verbal communication, including translating technical controls into business language and vice versa.
• Customer-service mindset for user support, with the ability to triage, prioritize, and resolve technical issues efficiently.

Nice to have

• Experience with BI tools (QuickSight, Tableau) for usage and cost reporting.
• Knowledge of financial markets and enterprise data systems

Key Skills

Education

Bachelor's degree