AI Platform Engineer IV

Capital Group•Charlotte, NC

9d•Hybrid

About The Position

As an AI Platform Engineer, you will design, build, and operate the foundational components of Capital Group’s enterprise AI platform, enabling secure, scalable, and responsible development and deployment of advanced AI and agentic solutions. You will work across the full stack—from data ingestion and vector databases to orchestration, agent frameworks, and user-facing APIs—empowering teams to deliver innovative AI-powered experiences. You will collaborate with security, FinOps, and engineering peers, as well as data scientists and ML engineers, to deliver robust, enterprise-ready AI capabilities. Your work will span the integration of cloud-native services, orchestration frameworks, agentic architectures, and responsible AI guardrails. You will play a critical role in the design and implementation of solutions based on the Model Context Protocol (MCP) and AI Gateway patterns.

Requirements

A bachelor’s degree in computer science, Engineering or a related technical field or relevant experience.
5+ years of general platform technology build experience or large, distributed systems
3+ years of experience building, operating AI/ML platforms
Hands-on experience with MLOps tools and ML frameworks
Well versed in vector databases and knowledge graph technologies
Familiarity with agentic AI frameworks and orchestration tools
Experience designing and implementing solutions based on the Model Context Protocol (MCP) and AI Gateway patterns
Proficiency in Python and/or other languages commonly used in AI/ML engineering
Experience with cloud platforms and container orchestration
An understanding of AI observability, model monitoring, and responsible AI practices
Experience implementing security, privacy, and compliance controls in AI/ML environments

Nice To Haves

Experience in regulated industries (e.g., financial services) and navigating governance, risk, and compliance for AI
Familiarity with AI-specific observability tools
Experience with agent orchestrators, agent-to-agent communication, and multi-agent systems
Exposure to AI guardrails and responsible AI frameworks (e.g., explainability, bias detection)
Experience with cost optimization and FinOps for AI/ML workloads
Familiarity with Agile and DevSecOps practices in AI/ML environments

Responsibilities

Build and maintain AI platform services: Develop data ingestion pipelines, feature stores, and ML workflows
Integrate vector databases, knowledge graphs, and model registries with governance
Implement automated ML-Ops pipelines for training, evaluation, and deployment
Design model serving infrastructure for real-time and batch inference and orchestrate agentic workflows using modular, extensible frameworks
Enable agentic workflows and secure connectivity using MCP and AI Gateway patterns
Integrate with agent orchestrators and support for agent-to-agent communication protocols
Ensure observability and responsible AI: Monitor model performance, data quality, and lineage
Implement logging, alerting, and rollback mechanisms
Apply explainability, fairness, and compliance guardrails
Experience with embedding security and compliance: Integrate encryption, IAM, and audit logging
Support compliance with regulatory and internal policies (e.g., GDPR, SOC 2, Responsible AI frameworks)
Partner with InfoSec and data governance teams to ensure safe and compliant use of data and models
Drive operational excellence: Apply SRE and FinOps practices for reliability and cost optimization
Automate infrastructure provisioning with IaC
Collaborate and enable teams: Partner with data scientists and engineers to deliver platform solutions
Develop APIs, SDKs, and self-service tools for rapid experimentation
Produce clear documentation, runbooks and architectural diagrams