AI Lead & C4 System Architect

• 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 and with 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.

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.