Project Overview
We are looking for an experienced AI Platform Engineer to drive enterprise AI platform enablement across engineering, governance, and user adoption. This role combines GenAI engineering, cloud platform development, security controls, cost governance, and user enablement to deliver scalable and compliant AI solutions.
The ideal candidate will have hands-on experience with LLMs, Agentic AI, MCP, RAG/Graph architectures, AWS cloud services, DevSecOps, and enterprise AI governance.
Key Responsibilities
AI Platform Enablement
- Lead enterprise AI platform onboarding for users, teams, and business use cases.
- Enable AI products across platforms including OpenAI, Gemini, Claude, Amazon Bedrock, and LLM gateways.
- Implement and support Agentic AI frameworks, MCP integrations, and AI platform capabilities.
- Drive adoption of responsible AI practices across the organization.
Governance, Security & Compliance
- Partner with Information Security, Risk, Legal, and Compliance teams to define AI platform guardrails.
- Translate governance requirements into technical controls including IAM, content filtering, logging, auditing, rate limits, and data protection.
- Maintain compliance documentation, audit evidence, and platform control reviews.
- Ensure adherence to enterprise DevSecOps and Responsible AI standards.
Cost & Usage Management
- Manage token budgets, credits, quotas, and rate limits across AI providers.
- Build dashboards and automated alerts for cost anomalies and usage spikes.
- Define allocation models that balance productivity with cost optimization.
- Monitor enterprise AI spend and improve usage transparency.
User Support & Enablement
- Provide day-to-day support for AI platform users and development teams.
- Troubleshoot skills, agents, MCP servers, API integrations, and prompt applications.
- Create onboarding guides, FAQs, runbooks, and self-service documentation.
- Improve platform reliability through operational monitoring and incident analysis.
Engineering & Delivery
- Design and develop scalable GenAI platform services using Python and AWS.
- Build LLM pipelines, Agentic workflows, MCP integrations, and Graph/RAG solutions.
- Implement Infrastructure as Code for platform provisioning and policy enforcement.
- Deliver platform initiatives using Agile (Scrum/Kanban) methodologies.
Required Skills
| Category | Skills |
|---|---|
| AI Platforms | OpenAI, Claude, Gemini, Amazon Bedrock |
| LLMs | GPT, Claude, Gemini, LLaMA, Prompt Engineering |
| Agentic AI | Agents, MCP, Skills, Graph/RAG Architectures |
| Cloud | AWS (EKS, Lambda, Fargate, EC2, Athena, Glue, Bedrock, Lake Formation) |
| Programming | Python, NumPy, Pandas, Boto3 |
| Infrastructure | Terraform, Docker, Puppet |
| Databases | PGVector, Weaviate, Milvus, Neo4j, Neptune |
| Testing | Ragas, Playwright, Selenium, Zephyr |
| DevSecOps | IAM, Logging, Audit Trails, Rate Limiting, CI/CD |
| Tools | JIRA, Confluence, JIRA Align |
Must-Have Qualifications
- 6+ years of progressive software or platform engineering experience.
- 1–2+ years working with AI platforms, cloud enablement, or emerging technologies.
- Experience implementing enterprise security and compliance controls.
- Strong knowledge of Agentic AI, MCP, RAG, and LLM application patterns.
- Hands-on AWS cloud development and Infrastructure as Code.
- Excellent stakeholder management across engineering, security, risk, and compliance teams.
- Strong communication and customer support mindset.
Nice to Have
- QuickSight or Tableau for AI usage and cost reporting.
- Financial markets or enterprise data systems experience.
- Enterprise AI governance and cost optimization expertise.