Durapid Technologies Private Limited
AI Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer with a contract length of "Unknown" and a pay rate of "Unknown." Key skills include 4+ years in DevOps/MLOps, strong Kubernetes experience, Python proficiency, and familiarity with AWS/GCP. Specific industry experience in ML infrastructure is required.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
October 15, 2025
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#Scala #Version Control #Data Science #Bash #MLflow #Grafana #DevOps #GitHub #Deployment #Monitoring #Security #GCP (Google Cloud Platform) #Kubernetes #Jenkins #Compliance #Infrastructure as Code (IaC) #Storage #Docker #Automation #AWS (Amazon Web Services) #Scripting #ML (Machine Learning) #Snowflake #Terraform #AI (Artificial Intelligence) #Prometheus #Cloud #Streamlit #Logging #Python
Role description
About The Role
We are looking for a highly skilled AI Infrastructure Engineer to join our growing team. This is a critical position responsible for designing, building, and maintaining scalable, secure, and production-ready infrastructure to support our AI and ML initiatives.
You will work at the intersection of Data Science, ML Engineering, and DevOps, enabling seamless model development, deployment, and monitoring across multiple environments including AWS, GCP (Vertex AI), and on-premises/virtualized setups.
Key Responsibilities
• Design, implement, and manage scalable and reliable AI/ML infrastructure and pipelines.
• Collaborate with data scientists and ML engineers to deploy, monitor, and optimize models in production.
• Develop and maintain CI/CD pipelines for ML workflows using GitHub Actions / Jenkins / Cloud Build.
• Implement infrastructure as code (IaC) using Terraform / CloudFormation.
• Optimize compute and storage resources across AWS and GCP (Vertex AI).
• Manage containerized deployments using Kubernetes / OpenShift.
• Build monitoring, logging, and alerting solutions for deployed AI/ML models.
• Ensure infrastructure security, scalability, and compliance with organizational standards.
Tech Stack / Tools We Use
• Cloud Platforms: AWS, GCP (Vertex AI), hybrid/virtualized environments
• Languages: Python, Bash
• Version Control: GitHub
• Containerization: Kubernetes, Docker, OpenShift
• Data & ML: Snowflake, Streamlit, Vertex AI, MLflow (preferred)
• CI/CD: GitHub Actions, Jenkins, Cloud Build
• IaC: Terraform, CloudFormation
• Monitoring: Prometheus, Grafana, ELK stack (plus)
Required Skills & Qualifications
• 4+ years of experience in DevOps, MLOps, or Platform Engineering roles.
• Strong hands-on experience with Kubernetes and cloud platforms (AWS/GCP).
• Proven experience in building and maintaining ML pipelines and infrastructure.
• Proficiency in Python scripting and automation.
• Familiarity with data platforms such as Snowflake.
• Understanding of security, compliance, and cost optimization in cloud environments.
• Excellent collaboration and problem-solving skills, with the ability to work across cross-functional teams.
Skills: infrastructure,ml,vertex
About The Role
We are looking for a highly skilled AI Infrastructure Engineer to join our growing team. This is a critical position responsible for designing, building, and maintaining scalable, secure, and production-ready infrastructure to support our AI and ML initiatives.
You will work at the intersection of Data Science, ML Engineering, and DevOps, enabling seamless model development, deployment, and monitoring across multiple environments including AWS, GCP (Vertex AI), and on-premises/virtualized setups.
Key Responsibilities
• Design, implement, and manage scalable and reliable AI/ML infrastructure and pipelines.
• Collaborate with data scientists and ML engineers to deploy, monitor, and optimize models in production.
• Develop and maintain CI/CD pipelines for ML workflows using GitHub Actions / Jenkins / Cloud Build.
• Implement infrastructure as code (IaC) using Terraform / CloudFormation.
• Optimize compute and storage resources across AWS and GCP (Vertex AI).
• Manage containerized deployments using Kubernetes / OpenShift.
• Build monitoring, logging, and alerting solutions for deployed AI/ML models.
• Ensure infrastructure security, scalability, and compliance with organizational standards.
Tech Stack / Tools We Use
• Cloud Platforms: AWS, GCP (Vertex AI), hybrid/virtualized environments
• Languages: Python, Bash
• Version Control: GitHub
• Containerization: Kubernetes, Docker, OpenShift
• Data & ML: Snowflake, Streamlit, Vertex AI, MLflow (preferred)
• CI/CD: GitHub Actions, Jenkins, Cloud Build
• IaC: Terraform, CloudFormation
• Monitoring: Prometheus, Grafana, ELK stack (plus)
Required Skills & Qualifications
• 4+ years of experience in DevOps, MLOps, or Platform Engineering roles.
• Strong hands-on experience with Kubernetes and cloud platforms (AWS/GCP).
• Proven experience in building and maintaining ML pipelines and infrastructure.
• Proficiency in Python scripting and automation.
• Familiarity with data platforms such as Snowflake.
• Understanding of security, compliance, and cost optimization in cloud environments.
• Excellent collaboration and problem-solving skills, with the ability to work across cross-functional teams.
Skills: infrastructure,ml,vertex
