Principle AI Lead & C4 System Architect

Location: Los Angeles CA and New York NY (resource must be onsite at any one of the locations, two days a week in office)

Must Have Skils: C4 Architecture & System Design, AI/ML Integration, Understanding of digital broadcast/visual media production operations

• Experience:
  
  8+ years of professional software engineering experience with at least 2+ years in a technical leadership or principal-level capacity.
• AI/ML Integration:
  Proven experience building agentic workflows, prompt engineering, integrating LLMs/VLMs into production systems, and utilizing AI evaluation frameworks.
• Cloud & Infrastructure:
  Deep, hands‑on expertise with AWS, Azure, and/or GCP serverless architectures.
• Backend Engineering:
  Expert‑level Python proficiency. Experience building high‑throughput APIs using FastAPI, managing database migrations, and handling async/multiprocessing workflows.
• Dev Ops & SDLC:
  Knowledge of Terraform IaC and CI/CD pipelines (e.g. GHA).
• Domain Knowledge:
  
  Strong domain understanding of digital broadcast production operations andwith Visual media workflows is highly desirable.
• Architecture:
  Strong expertise with the C4 architecture model , enterprise integration patterns and multimodel AI Orchestration.

Serves as the primary technical decision‑maker and lead architect across enterprise AI initiatives, multimodal systems, and distributed workflow platforms. Responsible for end‑to‑end technical leadership, defining and maintaining system architecture using the C4 model, directing delivery teams, and guiding the development of production‑grade agentic AI harnesses, backend microservices, and cloud infrastructure.

Core Responsibilities

System Architecture & C4 Modeling:
Define and maintain C4 model architecture diagrams (Context, Container, Component, Code) for distributed workflows. Oversee architectural reviews and code quality standards for standard microservices and non‑deterministic AI agent outputs.

Agentic AI & Multimodal System Design: Build and manage agentic AI harnesses from scratch (tool use, multi‑step chaining, reasoning, RAG, sandboxing, state management). Integrate LLMs, VLMs, and specialized APIs for automated asset manipulation and enterprise workflow automation.

Backend & Distributed Systems: Design scalable, high‑throughput backend microservices using Python and FastAPI on cloud serverless infrastructure. Architect distributed event‑driven workflows using cloud message queues, streams, and event buses (e.g., AWS Event Bridge, SQS, DynamoDB Streams)

AI Evaluation, Safety & Quality: Establish AI evaluation frameworks (offline regression evals, rubric scoring, AI‑as‑judge) and deterministic guardrails/content filters to prevent drift and hallucination.

Team Leadership: Direct delivery engineering teams and align technical strategy and execution roadmaps with client leadership.

Please feel free to reach out in case you need any additional details.