Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field
2-6+ years of experience in MLOps, DevOps, Data Engineering, or Machine Learning Engineering
Experience deploying ML models in cloud environments
Strong understanding of software engineering best practices
Familiarity with model lifecycle management and CI/CD processes
Responsibilities
Design and implement end-to-end ML pipelines
Deploy machine learning models to production environments
Build CI/CD pipelines for ML workflows
Automate model training, testing, deployment, and monitoring
Manage model versioning and experiment tracking
Monitor model performance, drift, and system health
Collaborate with Data Scientists and Software Engineers
Benefits
Work with cutting-edge ML and cloud technologies
Collaborative environment with cross-functional teams
Opportunities to optimize and scale ML infrastructure
Gain exposure to advanced topics like LLMs and RAG
Potential for career growth in a rapidly evolving field
Full Job Description
MLOps Engineer
Job Summary
We are looking for an MLOps Engineer to build, deploy, monitor, and maintain machine learning systems in production. The ideal candidate will work closely with data scientists, software engineers, and DevOps teams to automate the ML lifecycle and ensure scalable, reliable, and secure ML applications.
Key Responsibilities
Design and implement end-to-end ML pipelines.
Deploy machine learning models to production environments.
Build CI/CD pipelines for ML workflows.
Automate model training, testing, deployment, and monitoring.
Manage model versioning and experiment tracking.
Monitor model performance, drift, and system health.
Optimize infrastructure for scalable ML workloads.
Collaborate with Data Scientists and Software Engineers.
Implement security, governance, and compliance best practices.
Troubleshoot production ML issues.
Required Skills
Machine Learning
ML model lifecycle
Model deployment
Model monitoring
Feature engineering basics
Model versioning
Programming
Python
SQL
Bash scripting
Cloud Platforms
AWS
Azure
Google Cloud Platform (GCP)
MLOps Tools
MLflow
Kubeflow
Airflow
DVC
Weights & Biases (optional)
DevOps
Docker
Kubernetes
Git
Jenkins / GitHub Actions / GitLab CI
Data Engineering
Apache Spark
Kafka
ETL pipelines
Monitoring
Prometheus
Grafana
ELK Stack
Databases
PostgreSQL
MongoDB
Redis
Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
2-6+ years of experience in MLOps, DevOps, Data Engineering, or Machine Learning Engineering.
Experience deploying ML models in cloud environments.
Strong understanding of software engineering best practices.
Preferred Qualifications
Experience with Large Language Models (LLMs).
Experience with Retrieval-Augmented Generation (RAG).
Knowledge of vector databases.
Familiarity with Infrastructure as Code (Terraform or CloudFormation).
