Senior AI Platform Engineer

Job Description

Job Description

Title: Senior AI Platform Engineer
Location: New York City, NY/Hybrid
Duration: 12 Months
Experience required: 6 years
Interview Mode: Video

Job Description

As an AI Platform Engineer for AI & Emerging Tech, you will drive AI platform enablement across the enterprise. This role sits at the intersection of engineering, governance, and user enablement: you will partner with Information Security, Risk, Legal, and Compliance teams to define and agree on platform controls, implement those controls through configuration and code changes that make AI capabilities usable in a controlled enterprise environment. Provide hands-on technical support to AI products and platform users. You will also manage the cost of enterprise AI — ensuring tokens, credits, and platform spend are managed effectively, efficiently, and transparently.

The ideal candidate combines strong software engineering fundamentals with practical experience in GenAI platforms, LLM application patterns, cloud-native delivery, and enterprise risk management. This person should be comfortable translating policy and control requirements into technical implementation, while also helping users understand responsible AI usage, credit limits, access patterns, skills, agents, MCP integrations, platform capabilities, and cost-efficient consumption patterns.

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 sign-offs, 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.