AI Platform Engineer

Job summary

Builds and operates end-to-end generative AI solutions that translate business objectives into scalable, reliable, secure, and maintainable deployments. The role standardizes observability across AI/ML and data teams, maintains LLM gateways and usage controls, enables self-service deployment of AI agents and models, extends CI/CD and ML workflows, and deploys containerized workloads with Infrastructure Engineering.

Qualifications

• Bachelor’s degree in computer science, data science, engineering, or a related field, or equivalent experience required
• 2–4 years of experience in machine learning, AI development, data science, or related field required

Responsibilities

• Builds and refines end-to-end generative AI solutions aligned with defined business objectives
• Translates business requirements into AI model designs and technical implementation plans
• Implements best practices for model scalability, reliability, security, and maintainability
• Utilizes Python and related technologies to prepare, transform, and analyze data
• Collaborates with cross-functional stakeholders and communicates findings to technical and non-technical audiences
• Supports execution of AI integration initiatives aligned with enterprise roadmap
• Evaluates emerging AI capabilities and contributes to innovation initiatives
• Standardize observability practices across AI/ML and data teams covering logging, metrics, tracing and model/AI agent performance
• Implement and maintain LLM/AI gateways, cost tracking controls, rate-limiting and security guardrails to efficiently manage LLM usage
• Build a secure, self-service framework for engineering teams to deploy AI agents, models and services independently
• Extend existing CI/CD pipelines for code-first infrastructure management and ML workflows
• Design and deploy containerized ML workloads, partnering with Infrastructure Engineering on cluster provisioning, scaling and tuning

Skills

Degrees

Industry

Relocation