AI Lead & C4 System Architect

Must Have Skils: C4 Architecture & System Design, AI/ML Integration, Understanding of digital broadcast/visual media production operationsJob DescriptionExperience: 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.DevOps & 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. Job ResponsibilitiesServes 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 ResponsibilitiesSystem 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 EventBridge, 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.