ML &LLMOps Engineer - 300426-66

Role Summary

We are hiring an AI DevOps / LLMOps Engineer to build and operate cloud-native AI platforms and pipelines for production-grade, AI-powered applications.

This is a hands-on, ops-focused role requiring deep expertise in Cloud, DevOps, and MLOps/LLMOps, along with the ability to apply AI tools in day-to-day engineering workflows.

Must-Have Skills (Non-Negotiable)

Cloud & Infrastructure

• • Strong hands-on experience with AWS / Azure / GCP
  • Expertise in Infrastructure-as-Code (Terraform preferred)
  • Experience provisioning and managing production environments

MLOps / LLMOps

• • Experience building and operating ML/LLM pipelines
  • Knowledge of:
  • CI/CD for AI workloads
  • Model & prompt versioning
  • Monitoring, drift detection, retraining

CI/CD & Platform Engineering

• • Experience designing CI/CD pipelines
  • Automation of build → deploy → monitor workflows

Containers & Orchestration

• • Hands-on with Docker and Kubernetes
  • Managing scalable, distributed workloads

AI Platform Operations

• • Experience supporting:
  • LLM-based applications (RAG, APIs, pipelines)
  • Vector databases and inference systems
  • Managing compute, scaling, and performance

AI-Native Engineering (Critical)

• • Actively uses AI tools (LLMs, copilots, agents) in daily engineering work
  • Experience working with local/open-source LLMs
  • Applies AI to improve automation, debugging, and operational efficiency

Core Responsibilities

• • Provision and manage cloud infrastructure for AI applications
  • Build and maintain MLOps / LLMOps pipelines
  • Deploy and operate AI-powered applications in production
  • Implement monitoring, logging, and observability
  • Optimize cost, performance, and resource usage
  • Ensure reliability, scalability, and security
  • Collaborate with engineering teams to productionize AI systems

Required Experience

• • 5-10+ years in DevOps / Cloud / Platform Engineering
  • 2-4+ years in MLOps / LLMOps / AI platforms
  • Strong experience with:
  • Python / scripting
  • CI/CD tools
  • Infrastructure-as-Code

Preferred

• • Experience with vector databases
  • Exposure to LLM ecosystems
  • Familiarity with microservices / event-driven systems
  • Knowledge of AI governance and security