Staff GTM AI Engineer

Responsibilities

• Architectural Ownership

  • System Authority: Serve as the technical lead for our Seller Agentic platform. You own the architecture across business functions and are responsible for its long-term health and scalability.

  • Platform Evolution: Design and build the next-gen data/AI infrastructure. This includes robust integration layers, real-time intelligence pipelines, and service architectures that handle high-volume data without degradation.

  • Risk Mitigation: Anticipate scaling bottlenecks. You will define the roadmap for iterative modernization to ensure the platform is future-proofed before performance issues surface

• Technical Execution & Mentorship

  • Hands-on Leadership: While you are a strategic leader, you are also a practitioner. You will contribute to writing and shipping the code.

  • Engineering Excellence: Set the standards for the team. You will ensure that rapid prototyping is built on sound, sustainable foundations, teaching others how to manage complexity as the platform scales.

  • The "Golden Record" Strategy: Lead the data strategy to establish a single, trusted system of record for account intelligence, ensuring consistency across the revenue lifecycle.

• Engineering Outcomes You’ll Own

  • Scalable Architecture: Build a foundation that supports moving from one use case to dozens without regressions or downtime.

  • Systemic Trust: Ensure AI outputs are auditable, accurate, and consistent. You will turn "Trust" into a provable engineering metric, not a marketing claim.

  • Multiplier Effect: Your abstractions and API boundaries should enable junior engineers to ship faster and safer. You are here to amplify the team's output.

Requirements

• Experience: 7–10+ years in software engineering, with a proven track record of owning large-scale, distributed system architectures.

• Languages & Infra: Expert-level fluency in Python and modern cloud environments (AWS).

• Agentic Platforms: Demonstrated experience building on Agentic frameworks (e.g., LangGraph, Claude, Vertex AI, Workato).

• AI/ML Ops: Deep understanding of LLMs, RAG, vector databases, memory systems, and prompt engineering at scale.

  • Architecture: Mastery of Kubernetes, microservices, and high-throughput event-driven architectures.