Description

Key Responsibilities

• Program Execution: Drive the end-to-end technical delivery of agentic workflows and multi-agent solutions from concept to production/scaling.
• Technical Architecture Gating: Collaborate with AI Engineers and Architects to assess technical feasibility, evaluate agent reasoning frameworks (Chain-of-Thought, Tool/API integration), and ensure secure code execution environments.
• Vendor & Partner Integration: Lead technical governance and delivery tracking for strategic partners/vendors delivering resources for the AAM portfolio.
• Dependency & Risk Management: Proactively identify technical blockers, compliance risks (Data Governance/PII), and resource bottlenecks.
• Metrics & Evaluation: Establish standard technical evaluation frameworks to measure Agent performance, hallucination rates, and tool-use quality.

Mandatory Qualifications

• Agentic AI Expertise: Deep technical understanding of Agentic workflows, autonomous AI agents, multi-agent orchestration, and LLM-based reasoning (e.g., memory bank integration, tool calling, stateful sessions).
• Hands-on Framework Knowledge: Practical familiarity and experience with the Agent Development Kit (ADK) (e.g., Vertex AI) or other leading agent frameworks (e.g., LangChain, LangGraph, AutoGen).
• TPM Experience: 5+ years of experience managing complex, cross-functional technical programs or software development life cycles (SDLC) in an Agile environment.

Preferred Qualifications & Skills

• Cloud Infrastructure: Experience deploying containerized AI applications or agents on scalable cloud platforms (e.g., GCP, Vertex AI Agent Engine, GKE).
• Data & Analytics: Familiarity with data pipelines, vector databases (e.g., AlloyDB, BigQuery), or standard API/MCP Toolboxes for databases.
• Coding/Scripting Basics: Ability to read and prototype in Python or Java to effectively audit technical blockers or review simple agent logic.
• Vendor Governance Awareness: Understanding of vendor operations (VMO), rate card audits, or milestone-based delivery models is a strong plus.

Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related technical field.

Soft Skills for Success

• Strategic Communications: Ability to explain complex generative AI and agentic concepts to non-technical stakeholders and leadership.
• Ambiguity Resolution: Comfort operating in a rapidly changing AI/ML landscape where product definitions and frameworks evolve constantly

Key Skills

Education

Bachelor's or Master's degrees