Ai And Engineering Content Velocity
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- Agentic AI Development : Expand our core AI harnesses and pipelines. Integrate and evaluate state-of-the-art LLMs, Vision-Language Models (VLMs), and specialized APIs to autonomously manipulate various asset formats.
- Infrastructure & Dev Ops : Oversee and improve our Infrastructure as Code across all environments. Maintain and optimize CI/CD pipelines (Docker/Container Registry deployments).
- Mentorship & Code Quality : Set the standard for engineering excellence. Mentor senior and mid-level engineers, conduct rigorous architectural reviews, and maintain high standards for testing and AI evals.
- Cross-Stack Contribution : While heavily focused on backend and AI orchestration, Applies extensive knowledge of theories, practices and design matters.
- Service Development : Build and maintain scalable backend microservices using Python and Fast API, deployed as varying serverless infrastructure.
- AI Tooling & Integrations : Develop tools, constraints (guardrails), and deterministic functions that our AI agents use to edit visual assets. Integrate models on owned infrastructure for inference and integrate third‑party AI endpoints.
- Event-Driven Architecture : Implement complex distributed workflows for high-volume job orchestration and dispatching (e.g. Event Bridge, queues, Dynamo DB streams).
- Infrastructure as Code : Terraform contributions to provision and mange cloud resources safely and effectively across multiple environments.
- Full-Stack Contribution : Contribute to our modern backend orchestration or modern web frontends.
- Testing & CI/CD : Write robust unit and end-to-end tests to ensure the stability of both standard APIs and AI agent outputs, ensuring smooth deployments.
- 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 Fast API, managing database migrations, and handling async/multiprocessing workflows.
- Dev Ops & SDLC : Knowledge of Terraform Ia C and CI/CD pipelines (e.g. GHA).
- Domain Knowledge : Familiarity with image and video processing libraries. Visual rendering workflows is highly desirable.
- Architecture : Strong experience with the C4 model and other traditional design artifacts
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