AI DevOps Engineer

What You'll Do

• Design, build, and operate CI/CD and MLOps/LLMOps pipelines for deploying AI models and services across Azure, GCP, and AWS.
• Deploy and scale RAG systems end to end — embeddings, vector databases, retrieval and re-ranking pipelines, and evaluation.
• Stand up and operate multi-agent orchestration frameworks (e.g. LangGraph, CrewAI, AutoGen, or similar) in production, including tool integration, state management, and observability.
• Integrate and serve multimodal, vision, and computer vision capabilities — vision APIs, image/video understanding, and CV model inference.
• Own infrastructure-as-code, containerization, and orchestration (Terraform, Docker, Kubernetes) and GPU-backed model serving.
• Build monitoring, logging, cost tracking, and performance optimization for GenAI and LLM workloads.
• Partner with creative and design teams to embed AI into creative production pipelines — automating and extending workflows in tools like Adobe After Effects and Figma.
• Champion reliability, security, and reproducibility across the AI stack.

Required Qualifications

• 4–7 years of experience in DevOps / LLMOps / Platform Engineering with an GENAI & ML focus, including a track record of shipping systems to production.
• Hands-on deployment experience across at least two of Azure, GCP, and AWS (all three strongly preferred).
• Strong with Docker, Kubernetes, and Terraform (or equivalent IaC).
• Practical experience building RAG pipelines and working with vector databases and embeddings.
• Experience with multi-agent orchestration and modern LLM / agent frameworks.
• Familiarity with vision APIs and computer vision — integrating and serving vision or multimodal models.
• Strong scripting and automation skills in Python (plus comfort with shell / another language).
• CI/CD, observability, and infrastructure cost-management fundamentals.

Good to Have

• Background in media, creative AI, animation, or design production.
• Hands-on with Adobe After Effects (AFx) and Figma — including scripting, plugins, or workflow automation.
• Experience with generative media models (image, video, audio, or 3D generation).
• Understanding of creative production pipelines and how to integrate AI into them.
• Exposure to fine-tuning, model evaluation, or prompt engineering at scale.