MLOps Engineer

Quick Facts

• Role: MLOps Engineer

Description

You will build and maintain an AI/LLMOps platform for production deployment of machine learning models in the insurance domain. The work includes designing MLOps/LLMOps infrastructure on Azure, implementing CI/CD and continuous training pipelines, containerizing and deploying workloads to Kubernetes (AKS), and establishing monitoring/observability and governance for AI models. You will also support GenAI projects such as LLMs and RAG architectures.

Responsibilities

• Create and maintain scalable MLOps/LLMOps infrastructure for training and serving models using Azure Machine Learning, Azure AI Foundry, and Kubernetes (AKS)

• Implement ML-focused CI/CD/CT pipelines, including automated testing and data/model versioning (DVC, MLflow) and Continuous Training

• Build Docker images for AI/GenAI models and deploy them on Kubernetes in a hybrid setup with on-prem integration

• Set up advanced model monitoring, including Data Drift/Model Drift detection, plus logging and alerting for high availability

• Support AI Act compliance with auditable model tooling, lineage, and security controls (access management, encryption)

• Optimize Azure cloud resource usage, inference latency, and infrastructure scaling based on load

Requirements

• 3+ years of experience in DevOps, MLOps, or Software Engineering, including hands-on ML model work in production

• Advanced Docker and Kubernetes experience (cluster management, Helm charts, Ingress)

• Deep Azure knowledge, particularly Azure ML, AKS, and Azure Container Registry (or experience with GCP/AWS with fast onboarding into Azure)

• CI/CD experience using Azure DevOps, GitHub Actions, or Jenkins, with ML pipeline steps (e.g., model training)

• Strong Python and Bash/Shell scripting

• MLOps tooling experience, including MLflow and/or Kubeflow, or equivalent cloud-native lifecycle management

• Infrastructure as Code experience with Terraform, Bicep, or Ansible

Benefits

• Opportunity to build a modern AI platform from scratch with real influence on architecture

• Work with an up-to-date AI/MLOps stack (Azure ML, Azure AI Foundry, AKS, MLflow, Docker, Terraform)

• GenAI experience including LLMs and RAG architectures

• Cross-functional growth at the intersection of Data Science, AI, and Cloud Engineering

• Build and co-create an environment for production-scale AI model deployment

• Modern MLOps/LLMOps practices: automation, monitoring, observability, and model governance