Mlops Enginner

**Job Title**: MLOps Engineer

**Experience Required**: 0-2 Years

**Job Overview**

We are seeking a motivated and detail-oriented **MLOps Engineer** to join our AI team. In this role, you will work closely with AI Developers to deploy, monitor, and manage Artificial Intelligence, Generative AI & machine learning projects on **AWS Cloud Services** and **in-house physical servers**. You will play a key role in automating and optimizing AI/ML model deployment pipelines, ensuring high availability, scalability, and performance in production environments.

**Key Responsibilities**

**Model Deployment & Infrastructure**
- Assist in deploying machine learning models using AWS services such as SageMaker, Lambda, EC2, and ECS/EKS.
- Deploy and maintain AI/ML models on **on-premise physical servers**, ensuring secure and efficient resource utilization.
- Work with containerization tools like Docker and Kubernetes to deploy scalable AI/ML models across both cloud and on-premise environments.
- Build and manage CI/CD pipelines using GitHub Actions, GitLab, or similar tools for model versioning, deployment, and rollback.

**Collaboration & Support**
- Collaborate with AI Developers and Data Scientists to ensure a smooth transition from development to production.
- Participate in architectural discussions and provide infrastructure support for AI and Data Science projects.

**Monitoring & Maintenance**
- Set up monitoring tools and alerts (e.g., AWS CloudWatch, Prometheus, Grafana) for model performance and infrastructure health.
- Manage logs, performance metrics, and model retraining workflows for both cloud and on-premise environments.

**Automation & Optimization**
- Automate AI/ML workflows including data preprocessing, training, validation, and deployment.
- Implement Infrastructure as Code (IaC) using Terraform, AWS CloudFormation, or similar tools.

**Security & Compliance**
- Ensure security best practices for data access, encryption, and user management.
- Maintain compliance with internal and external data governance standards.

**Required Skills**:

- Strong understanding of cloud platforms, especially AWS.
- Experience in deploying and managing machine learning models on **AWS Cloud Services and on-premises physical servers**:

- Proficiency in Python and scripting for automation.
- Experience with SQL, NoSQL, data management, and data modeling.
- Knowledge of Docker, Kubernetes, APIs, and webhooks.
- Experience with Git, GitHub, GitLab, and CI/CD using GitHub Actions or GitLab Pipelines.
- Familiarity with CI/CD pipelines and DevOps best practices.
- Good understanding of machine learning workflows and deployment practices.

**Preferred Skills**
- Exposure to MLOps tools like Kubeflow, MLflow, or AWS SageMaker Pipelines.
- Basic understanding of data streaming tools (e.g., Kafka, Kinesis).
- Familiarity with data architecture and ETL pipelines.
- Strong analytical, troubleshooting, and communication skills.

**Educational Qualifications**
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.