Senior AI Platform Engineer

Role Overview

Senior AI Platform Engineer to build and scale enterprise AI infrastructure, including AI agents, automation workflows, and integration capabilities. You will design foundations that help teams adopt AI safely, efficiently, and at scale.

Responsibilities

• Own the design, development, and optimization of internal AI platforms, agent systems, integrations, and automation solutions.
• Design and develop multi-step AI agents using reasoning, tool usage, automation, and human-in-the-loop processes.
• Build a secure tool/context layer to grant AI access to internal systems, data sources, and business apps via APIs, MCP servers, and retrieval systems.
• Develop and maintain MCP servers for external SaaS integrations and internal tooling with production readiness, security, and reliability.
• Implement authentication, authorization, and credential management using secure identity patterns and OAuth-based integrations.
• Define engineering standards for AI integrations (deployment, monitoring, logging, access controls, security requirements).
• Build plugins/skills and workflow extensions to automate repetitive processes and improve team productivity.
• Improve internal data platforms and analytics environments; optimize data pipelines/structures for AI use cases.
• Create usage tracking and reporting to measure AI adoption and guide platform investment.
• Optimize AI infrastructure costs (compute, storage, networking, operational costs).
• Apply security and compliance best practices for responsible, scalable, and auditable AI usage.
• Collaborate cross-functionally (engineering, data, operations, business) to prioritize needs and deliver production outcomes.

Requirements

• 8+ years of backend engineering experience building scalable software systems.
• Hands-on experience with AI agent frameworks, LLM integrations, MCP servers, and/or AI automation workflows.
• Strong backend fundamentals: APIs, integrations, and distributed systems.
• Experience with REST APIs, OAuth 2.0, credential management, and cloud authentication patterns.
• Experience deploying and operating services using Kubernetes (or similar container infrastructure).
• Background in data engineering, analytics platforms, or business intelligence.

Nice to Have

• Familiarity with Databricks or similar data warehouse/data lake technologies.