AI Platform Engineer Lead
About The Position
As a Lead AI Platform Engineer, you will serve as a technical SME and lead workstreams to 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 architecture, 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. Additionally, you will mentor and lead cross-functional initiatives, guiding engineers and stakeholders to deliver impactful solutions and foster a collaborative, inclusive culture.
Requirements
- You have a bachelor's degree in computer science, Engineering or a related technical field or relevant experience.
- You have 8+ years of leading general platform technology build experience or large, distributed systems with ability to drive architectural decisions and influence platform strategy.
- You have 3+ years of experience building, operating AI/ML platforms
- You have demonstrated hands-on experience with MLOps tools and ML frameworks
- You have experience with vector databases and knowledge graph technologies
- You possess proven experience leading technical teams and delivering enterprise-scale AI/ML platforms.
- You have experience with agentic AI frameworks and orchestration tools
- You are proficient in designing and implementing solutions based on the Model Context Protocol (MCP) and AI Gateway patterns
- You have experience coding in Python and/or other languages commonly used in AI/ML engineering
- You have experience with cloud platforms and container orchestration
- You understand AI observability, model monitoring, and responsible AI practices
- You have 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
- Experience defining platform roadmaps and aligning with business objectives.
Responsibilities
- 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
- Monitor model performance, data quality, and lineage
- Implement logging, alerting, and rollback mechanisms
- Apply explainability, fairness, and compliance guardrails
- Integrate encryption, IAM, and audit logging
- Support compliance with regulatory and internal policies (e.g., GDPR, SOC 2, Responsible AI frameworks)
- Champion security, privacy, and regulatory adherence in partnership with InfoSec
- Apply SRE and FinOps practices for reliability and cost optimization
- Set best practices for infrastructure automation and scalability
- Lead cross- functional workstreams with data scientists and ML engineers to deliver platform solutions
- Develop APIs, SDKs, and self-service tools for rapid experimentation
- Produce clear documentation, runbooks and architectural diagrams
Benefits
- In addition to a highly competitive base salary, per plan guidelines, restrictions and vesting requirements, you also will be eligible for an individual annual performance bonus, plus Capital's annual profitability bonus plus a retirement plan where Capital contributes 15% of your eligible earnings.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Industry
Securities, Commodity Contracts, and Other Financial Investments and Related Activities
