MLOps Engineer

Role Overview

We are looking for a skilled MLOps Engineer to support the end-to-end lifecycle of machine learning models,

including deployment, monitoring, governance, and CI/CD integration. The ideal candidate will work closely with

data scientists, platform engineers, and DevOps teams to operationalize AI/ML workloads at scale.

Key Responsibilities

• Support the entire ML model lifecycle, including training, validation, deployment, monitoring, and retraining

• Implement CI/CD pipelines for machine learning and AI workloads

• Ensure model governance, versioning, reproducibility, and compliance

• Deploy and manage ML workloads using containerization and orchestration platforms

• Integrate and manage ML pipelines using cloud-native and open-source tools

• Monitor model performance, system health, and logs using observability tools

• Collaborate with cross-functional teams to improve reliability, scalability, and security of ML platforms

• Automate infrastructure provisioning and configuration using Infrastructure as Code (IaC)

Required Skills & Technical Expertise

• Strong programming skills in Python, SQL, and scripting (Bash / PowerShell)

• Hands-on experience with ML frameworks: TensorFlow, PyTorch, Keras

• Experience with containerization and orchestration: Docker, Kubernetes, OpenShift

• ML lifecycle and pipeline tools: MLflow, Kubeflow, SageMaker Pipelines, Vertex AI Pipelines

• Monitoring and logging tools: ELK Stack, Prometheus, Splunk

• Version control systems: Git, GitHub, GitLab

• Infrastructure automation using Terraform

Good-to-Have

• Experience supporting AI workloads in cloud environments

• Understanding of model governance, compliance, and security best practices

• Exposure to large-scale, distributed ML systems

Qualifications

• Bachelors degree in Computer Science, Engineering, or a related field

• Relevant experience in MLOps, DevOps, or ML Engineering roles