AI DevOps Engineer

The purpose of this role is to deliver analysis inline with client business objectives, goals, and to maintain, develop and exceed client performance targets.

Job Description:

We're looking for an AI DevOps Engineer who lives at the intersection of production AI/ML infrastructure and creative technology. You'll own the deployment, scaling, and reliability of our generative AI systems — RAG pipelines, multi-agent workflows, multimodal and vision models — across all three major clouds. Just as importantly, you understand the creative side: you can plug AI into real production pipelines built on tools like After Effects and Figma, and you speak the language of designers, animators, and media teams.

This is a builder role for someone who's equally comfortable writing Terraform and reasoning about a vector retrieval pipeline, and who gets genuinely excited when AI infrastructure ships something creative into the world.

• 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.

• 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.

• 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.

In your first few months, you'll have deployed at least one GenAI service to production across our cloud environment, made it observable and cost-efficient, and worked directly with a creative team to ship an AI-powered feature into their workflow.

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