Capital Group
Overview
As the Head of AI Platform Engineering, you will lead the vision, strategy, and delivery of Capital Group’s enterprise AI platform. You will architect secure, scalable, and resilient foundations that accelerate innovation while meeting rigorous standards for security, compliance, and operational excellence. You will collaborate across engineering, security, and risk teams to turn cutting-edge AI into measurable business value—governing responsibly and optimizing for speed, reliability, cost, and ROI. You will build and lead a diverse, high-performing team that develops and operates Capital Group’s AI platform. You will own the end-to-end design, delivery, and evolution of a secure, scalable platform that empowers teams across the organization to build and deploy AI applications at scale. You bring deep expertise in platform engineering and a strong understanding of AI/ML systems, enabling developers and business partners to innovate responsibly. You will work closely with Product Management, Architecture, and Information Security to integrate AI with care, enforce governance, and ensure compliance. As the principal engineering authority for AI, you will make strategic decisions that balance agility, control, and enterprise risk. Key Responsibilities Define and Execute Platform Strategy: set the vision, roadmap, and operating model for Capital’s Custom Development AI platform, aligning with enterprise architecture and business goals while balancing speed, scale, and governance. Build and Evolve AI Capabilities: lead the design, development, and deployment of scalable, reusable AI services—covering model serving, orchestration, guardrails, and developer enablement through SDKs, APIs, and CI/CD pipelines. Ensure Security, Compliance, and Responsible AI: embed security, privacy, and ethical guardrails into all layers of the platform, enforce governance standards, and partner with InfoSec and risk teams to meet regulatory and policy requirements. Drive Operational Excellence and Reliability: establish SRE principles, observability, FinOps practices, and service management processes that ensure high availability, cost efficiency, and resilience of AI services. Lead Cross-Functional Collaboration and Team Development: partner with product, architecture, and security teams to deliver enterprise-ready AI solutions, while mentoring and growing an inclusive, high-performing platform engineering team. Required Qualifications You have 10+ years of experience designing and implementing complex distributed systems and enterprise architectures, with a proven track record of building scalable, resilient platforms. You have 5+ years of experience operating public and private clouds (AWS, Azure), leveraging cloud-native services, containers (Docker/Kubernetes), and serverless technologies, with exposure to hybrid or multi-cloud environments. You bring strong software engineering foundations (10+ years), including coding, APIs, integration patterns, and maintainable design, with proficiency in languages such as Python, Java, or Go. You are skilled in building automated CI/CD pipelines (e.g., GitHub/GitLab, Jenkins), implementing Infrastructure as Code (Terraform, CloudFormation), and embedding DevSecOps controls in the path to production. You have 5+ years of experience applying application and infrastructure security—including IAM, network security, encryption/tokenization—and operating against compliance frameworks such as SOC 2 and GDPR. You demonstrate a solid understanding of AI, large language models, and end-to-end ML workflows (training, fine-tuning, inference), with hands-on MLOps and production model deployment experience. You are adept at troubleshooting complex cross-stack issues using data and telemetry and driving timely resolution. You lead by example—mentoring engineers, guiding cross-functional initiatives, and fostering a collaborative, inclusive culture. You communicate complex technical concepts clearly to non-technical stakeholders and executives, producing well-structured proposals, architectural diagrams, and documentation. Preferred Qualifications You have experience operating in large, regulated enterprises (ideally financial services), and navigating governance, risk, and compliance when introducing new technologies like AI. You are proficient in automating platform provisioning and configuration with Infrastructure as Code and config management (Terraform, CloudFormation, Ansible), and practicing GitOps (e.g., Argo CD, Flux). You are familiar with observability tools for distributed systems and AI platforms such as Datadog, Grafana, or Prometheus, and AIOps for proactive anomaly detection. You are familiar with function and purpose of key AI platform components such as AI gateways (Kong, Databricks Mosaic AI Gateway, custom API orchestration), Model Orchestration (Examples LangChain, LlamaIndex, DSPy, etc.) You have a working familiarity with Security and AI Guardrails including Snyk, Guardrails AI, Bedrock Guardrails. You have experience implementing FinOps practices, including cost allocation tagging, usage analytics, and optimization strategies, and partnering with finance to report on cloud/AI spend and ROI. You are comfortable working with ITSM tools and frameworks (e.g., ServiceNow, ITIL) and integrating automated workflows for incident and change management. You demonstrate mastery of Agile delivery (Scrum/Kanban) and DevSecOps practices, embedding security throughout the SDLC. You have experience operating Kubernetes at scale (cloud or on-prem), and working with service mesh, API gateways, and related cloud-native components. You collaborate effectively with data science teams and work with ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Kubeflow), including model versioning, feature stores, and pipeline automation. You are familiar with security benchmarks and frameworks (CIS Benchmarks, NIST CSF) and contribute to risk assessments and threat modeling for new services. We are an equal opportunity employer. We comply with all federal, state and local laws that prohibit discrimination in employment. This includes protections for race, religion, color, national origin, sex, age, disability, and other protected characteristics. EEO statements
This job description is not an offer of employment and is subject to change. All offers of employment are contingent upon background checks and other regulatory requirements.
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As the Head of AI Platform Engineering, you will lead the vision, strategy, and delivery of Capital Group’s enterprise AI platform. You will architect secure, scalable, and resilient foundations that accelerate innovation while meeting rigorous standards for security, compliance, and operational excellence. You will collaborate across engineering, security, and risk teams to turn cutting-edge AI into measurable business value—governing responsibly and optimizing for speed, reliability, cost, and ROI. You will build and lead a diverse, high-performing team that develops and operates Capital Group’s AI platform. You will own the end-to-end design, delivery, and evolution of a secure, scalable platform that empowers teams across the organization to build and deploy AI applications at scale. You bring deep expertise in platform engineering and a strong understanding of AI/ML systems, enabling developers and business partners to innovate responsibly. You will work closely with Product Management, Architecture, and Information Security to integrate AI with care, enforce governance, and ensure compliance. As the principal engineering authority for AI, you will make strategic decisions that balance agility, control, and enterprise risk. Key Responsibilities Define and Execute Platform Strategy: set the vision, roadmap, and operating model for Capital’s Custom Development AI platform, aligning with enterprise architecture and business goals while balancing speed, scale, and governance. Build and Evolve AI Capabilities: lead the design, development, and deployment of scalable, reusable AI services—covering model serving, orchestration, guardrails, and developer enablement through SDKs, APIs, and CI/CD pipelines. Ensure Security, Compliance, and Responsible AI: embed security, privacy, and ethical guardrails into all layers of the platform, enforce governance standards, and partner with InfoSec and risk teams to meet regulatory and policy requirements. Drive Operational Excellence and Reliability: establish SRE principles, observability, FinOps practices, and service management processes that ensure high availability, cost efficiency, and resilience of AI services. Lead Cross-Functional Collaboration and Team Development: partner with product, architecture, and security teams to deliver enterprise-ready AI solutions, while mentoring and growing an inclusive, high-performing platform engineering team. Required Qualifications You have 10+ years of experience designing and implementing complex distributed systems and enterprise architectures, with a proven track record of building scalable, resilient platforms. You have 5+ years of experience operating public and private clouds (AWS, Azure), leveraging cloud-native services, containers (Docker/Kubernetes), and serverless technologies, with exposure to hybrid or multi-cloud environments. You bring strong software engineering foundations (10+ years), including coding, APIs, integration patterns, and maintainable design, with proficiency in languages such as Python, Java, or Go. You are skilled in building automated CI/CD pipelines (e.g., GitHub/GitLab, Jenkins), implementing Infrastructure as Code (Terraform, CloudFormation), and embedding DevSecOps controls in the path to production. You have 5+ years of experience applying application and infrastructure security—including IAM, network security, encryption/tokenization—and operating against compliance frameworks such as SOC 2 and GDPR. You demonstrate a solid understanding of AI, large language models, and end-to-end ML workflows (training, fine-tuning, inference), with hands-on MLOps and production model deployment experience. You are adept at troubleshooting complex cross-stack issues using data and telemetry and driving timely resolution. You lead by example—mentoring engineers, guiding cross-functional initiatives, and fostering a collaborative, inclusive culture. You communicate complex technical concepts clearly to non-technical stakeholders and executives, producing well-structured proposals, architectural diagrams, and documentation. Preferred Qualifications You have experience operating in large, regulated enterprises (ideally financial services), and navigating governance, risk, and compliance when introducing new technologies like AI. You are proficient in automating platform provisioning and configuration with Infrastructure as Code and config management (Terraform, CloudFormation, Ansible), and practicing GitOps (e.g., Argo CD, Flux). You are familiar with observability tools for distributed systems and AI platforms such as Datadog, Grafana, or Prometheus, and AIOps for proactive anomaly detection. You are familiar with function and purpose of key AI platform components such as AI gateways (Kong, Databricks Mosaic AI Gateway, custom API orchestration), Model Orchestration (Examples LangChain, LlamaIndex, DSPy, etc.) You have a working familiarity with Security and AI Guardrails including Snyk, Guardrails AI, Bedrock Guardrails. You have experience implementing FinOps practices, including cost allocation tagging, usage analytics, and optimization strategies, and partnering with finance to report on cloud/AI spend and ROI. You are comfortable working with ITSM tools and frameworks (e.g., ServiceNow, ITIL) and integrating automated workflows for incident and change management. You demonstrate mastery of Agile delivery (Scrum/Kanban) and DevSecOps practices, embedding security throughout the SDLC. You have experience operating Kubernetes at scale (cloud or on-prem), and working with service mesh, API gateways, and related cloud-native components. You collaborate effectively with data science teams and work with ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Kubeflow), including model versioning, feature stores, and pipeline automation. You are familiar with security benchmarks and frameworks (CIS Benchmarks, NIST CSF) and contribute to risk assessments and threat modeling for new services. We are an equal opportunity employer. We comply with all federal, state and local laws that prohibit discrimination in employment. This includes protections for race, religion, color, national origin, sex, age, disability, and other protected characteristics. EEO statements
This job description is not an offer of employment and is subject to change. All offers of employment are contingent upon background checks and other regulatory requirements.
#J-18808-Ljbffr