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).

- Solid 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.

📌 AI DevOps Engineer (Bengaluru)
🏢 AMS
📍 Bengaluru