terraformdockerkubernetespythonazuregcpawsragvector databasesembeddingsmulti-agent orchestrationlanggraphcrewAIautogenvision APIscomputer visionci/cdmlopsllmopsgpuadobe after effectsfigmagenaiai infrastructurecreative ai
Key details
Salary
Not specified
Employment type
Full-time
Seniority
Mid-level
Years experience
3-5
Location
Gurugram, India
Full job description
Seeking AI DevOps Engineer to deploy, scale, and maintain generative AI systems (RAG pipelines, multi-agent workflows, multimodal and vision models) across Azure, GCP, and AWS. Responsibilities include building CI/CD and MLOps/LLMOps pipelines, operating multi-agent orchestration frameworks, integrating vision and multimodal AI capabilities, managing infrastructure-as-code, containerization, GPU-backed model serving, and optimizing GenAI workloads. Collaborate with creative teams to embed AI into production pipelines using tools like Adobe After Effects and Figma. Requires 4-7 years experience in DevOps/LLMOps/Platform Engineering with GenAI & ML focus, hands-on cloud deployment experience, strong skills in Docker, Kubernetes, Terraform, Python scripting, and familiarity with RAG pipelines, vector databases, multi-agent frameworks, and vision APIs.
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
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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
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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
Requirements
4–7 years of experience in DevOps / LLMOps / Platform Engineering with a GENAI & ML focus
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
Tech stack
TerraformDockerKubernetesPythonShell scriptingAzureGCPAWSRAG pipelinesvector databasesembeddingsmulti-agent orchestration frameworksLangGraphCrewAIAutoGenvision APIscomputer visionCI/CDMLOpsLLMOpsinfrastructure-as-codecontainerizationGPU-backed model servingAdobe After EffectsFigma
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