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Job Description
Structured overview of role & requirementsAbout This Role
Own deployment, scaling, and reliability of generative AI systems (RAG pipelines, multi-agent workflows, multimodal and vision models) across Azure, GCP, and AWS.
Design, build, and operate CI/CD and MLOps/LLMOps pipelines and infrastructure, including Terraform, Docker, Kubernetes, and GPU-backed model serving.
Partner with creative teams to embed AI into production pipelines involving tools like Adobe After Effects and Figma.
Minimum Requirements
4–7 years of experience in DevOps / LLMOps / Platform Engineering with GenAI & ML focus and production system deployment.
Hands-on deployment experience with at least two of the three clouds: Azure, GCP, AWS (all three preferred).
Proficiency in Docker, Kubernetes, Terraform (or equivalent IaC), and Python scripting.
Practical experience with RAG pipelines, vector databases, embeddings, multi-agent orchestration frameworks, and vision APIs or computer vision integration.
Ideal Candidate Profile
Experienced engineer comfortable straddling complex AI infrastructure and creative technology integration in production environments.
Technical expertise in multi-cloud AI deployment, infrastructure-as-code, container orchestration, and ML model serving at scale.
Able to collaborate effectively with creative/design teams to embed and operationalize AI within media or animation workflows.