Platform Engineer III

“I am the person Capital Group is looking for”

• Design, build, and operate enterprise AI platform capabilities supporting Generative AI, Retrieval-Augmented Generation (RAG), and agentic workloads.
• Develop scalable data ingestion pipelines, knowledge ingestion workflows, and AI data services.
• Integrate vector databases, embeddings, knowledge graphs, and enterprise knowledge repositories with appropriate governance controls.
• Design and implement retrieval frameworks supporting enterprise search, semantic search, and RAG patterns.
• Build and operate AI services using Amazon Bedrock, including foundation model integrations, Bedrock Knowledge Bases, Guardrails, and inference capabilities.
• Design and support agentic architectures utilizing AWS Agent Core and other enterprise agent platforms.
• Implement model serving infrastructure for real-time and batch inference workloads.
• Enable secure agentic workflows through Model Context Protocol (MCP), tool orchestration frameworks, and agent-to-agent communication patterns.
• Design and implement AI Gateway capabilities using technologies such as Kong AI Gateway to provide centralized authentication, routing, governance, observability, rate limiting, and policy enforcement for AI workloads.
• Develop APIs, SDKs, reusable platform services, and self-service capabilities that accelerate AI adoption across engineering teams.

• Monitor model performance, application behavior, agent execution, and service reliability.
• Implement logging, tracing, alerting, rollback, and operational recovery mechanisms.
• Monitor AI usage patterns, token consumption, latency, throughput, and AI Gateway telemetry.
• Implement observability solutions that provide visibility into prompts, responses, model behavior, agent interactions, and platform health.
• Apply explainability, fairness, governance, and compliance guardrails consistent with Responsible AI principles.
• Support model evaluation, benchmarking, experimentation, and lifecycle management processes.

• Design and implement secure AI platform architectures using cloud-native security controls.
• Integrate encryption, IAM, secrets management, and audit logging capabilities.
• Implement secure access patterns through AI Gateway platforms including authorization, policy enforcement, prompt security controls, and data protection measures.
• Support compliance with regulatory and internal governance frameworks, including privacy, security, and Responsible AI requirements.
• Partner with Information Security, Risk, Compliance, and Data Governance teams to ensure safe and compliant use of enterprise data and AI services.
• Enable governance for models, agents, prompts, tools, and enterprise knowledge sources.

• Apply Site Reliability Engineering (SRE) practices to ensure reliability, scalability, and operational maturity.
• Apply Fin Ops principles to optimize AI platform utilization, model consumption, and cloud spending.
• Automate infrastructure provisioning and management using Infrastructure as Code (IaC).
• Establish operational standards, platform runbooks, SLA/SLO metrics, and support procedures.
• Drive continuous improvements in platform security, performance, resiliency, and developer experience.

• Partner with software engineers, platform engineers, architects, and product teams to deliver enterprise AI solutions.
• Consult with application teams on AI platform integration patterns and best practices.
• Create reference architectures, reusable patterns, and implementation guidance.
• Develop documentation, runbooks, architectural diagrams, and operational standards.
• Mentor…