Overview
Design, build, and maintain core engineering components of the Enterprise Agentic Framework and Intelligence Platform. Act as a technical anchor within the AI COE, providing hands-on expertise to Line of Business teams building on enterprise AI platforms.
Key Responsibilities
- Design, build, and maintain core engineering components of the Enterprise Agentic Framework and Intelligence Platform, including its model gateway, enterprise agent hub, and accelerator capabilities.
- Lead development of atomic agents, agent orchestration patterns, and the MCP hub, ensuring interoperability across the enterprise agentic backbone.
- Own the engineering of agent harnesses across the platform, covering orchestration, memory, observability, and evaluation.
- Establish reference architectures, technical blueprints, and reusable engineering patterns.
- Drive engineering quality across the platform's tool registry and the broader tools and accelerators portfolio.
- Act as a technical anchor within the AI COE, providing hands-on expertise to Line of Business teams.
- Develop templates, internal libraries, and API/client generation approaches.
- Lead technical enablement through workshops, architecture walkthroughs, and knowledge-sharing sessions.
- Support the "make the governed path the easiest path" platform philosophy.
- Ensure all agentic and AI tooling solutions align with enterprise standards for security, data classification, and responsible AI practice.
- Partner with AI governance and data governance functions to embed guardrails, evaluation frameworks, and quality checks.
- Work closely with the Enterprise Architecture and Office of Information Security teams.
- Contribute technical input to platform accreditation and security review processes.
- Provide technical oversight and quality assurance to delivery partners, contractors, and vendor teams.
- Partner closely with Product Owners across Data and AI Platforms, Data and Analytical Engineering, and Tools and Accelerators.
- Stay current with emerging developments in agentic AI, MCP, multimodal systems, and enterprise AI engineering.
Required Experience
- Typically requires a Master's degree with 5 years of experience or a Bachelors Degree with a minimum of 7 years of relevant experience, or equivalent combination of education and experience.
- 3+ years of experience in AI engineering and/or machine learning at enterprise scale.
Qualifications
- SAFe or other relevant Agile certifications.
- Industry-recognized certifications in AI Engineering, Platform Architecture, Cloud Technologies, or related disciplines.
