AI Devops Engineer

Spore N Sprouts; Gurugram District; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Merkle in Gurugram, India seeks an AI DevOps Engineer who bridges production AI/ML infrastructure and creative technology. You will own deployment, scaling, and reliability of generative AI systems across Azure, GCP, and AWS, and integrate AI into creative production pipelines with After Effects and Figma.

This builder role requires Terraform, Docker, Kubernetes, and GPU-backed model serving, plus Python scripting.

Qualifications

• 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 (three preferred).
• Strong with Docker, Kubernetes, and Terraform (or equivalent IaC).
• Experience with RAG pipelines, vector databases, embeddings, and multi‑agent orchestration.
• Familiarity with vision APIs and computer vision; experience with Python scripting and automation.
• CI/CD, observability, and infrastructure cost‑management fundamentals.

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, 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.

Skills

GenAI & ML

Docker

Kubernetes

Terraform

Python scripting

CI/CD pipelines

Observability

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