AI Devops Engineer

Dentsu Global Services; Gurugram District; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Dentsu Global Services is seeking an AI DevOps Engineer to own the deployment, scaling, and reliability of generative AI systems across cloud providers. You’ll work at the intersection of production AI and creative technology, connecting Terraform, Docker, Kubernetes, and GPU-backed serving with real production pipelines in After Effects and Figma.

You will design end-to-end MLOps pipelines, manage multi-agent orchestration, and ensure observability, cost-efficiency, and security across GenAI

Qualifications

• 4-7 years of experience in DevOps/LLMOps/Platform Engineering with a GENAI & ML focus.
• Hands-on deployment experience across at least two cloud platforms (Azure, GCP, AWS).
• Strong with Docker, Kubernetes, and Terraform (or equivalent IaC).
• Experience building RAG pipelines and working with vector databases and embeddings.

Responsibilities

• 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 in production with tool integration and observability.
• Integrate and serve multimodal, vision, and computer vision capabilities; CV model inference.
• Own infrastructure-as-code, containerization, and orchestration 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 (e.g., After Effects, Figma).
• Champion reliability, security, and reproducibility across the AI stack.

Skills

GenAI & ML focus

LLMOps / Platform Engineering

Python scripting

CI/CD fundamentals

Observability

Cost management

Automation

DevOps mindset

Multi-agent orchestration

Tools

Docker

Kubernetes

Terraform

Azure

GCP

AWS

LangGraph

CrewAI

AutoGen

Job description

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

About The Role

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.

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.
• 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.

Required Qualifications

• 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.

Good to Have

• 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.

What Success Looks Like

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.

Location

DGS India - Gurugram - Golf View Corporate Towers

Brand

Merkle

Time Type

Full time

Contract Type

Permanent#DGS