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Metro location, mid-level experience, and recognizable employer yield medium competition.
Highly specialized GenAI/LLMOps, multi-cloud, and creative-pipeline skills reduce cross-industry transferability.
Explicit 4–7 years plus multi-cloud, Kubernetes, RAG, vector DBs and GPU serving make filters stringent.
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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.
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.
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.