AI Devops Engineer
Merkle (a dentsu company) (company site, opens in a new tab)4–7 yrsOn-siteDirect career pageEst. 18–30 LPA
Posted on 25 Aug
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Job description
Structured overview of role & requirements
About 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.