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If you are a DevOps Engineer looking for excitement, challenge and stability in your work, then you would be glad to come across this page.
We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long term project. Here are a few details.
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Location:Charlotte, NC.
Exp: 3+ Years
We are seeking a highly skilled DevOps Engineer with a focus on supporting AI/ML workloads in containerized environments . The ideal candidate will have strong experience in deploying and managing distributed data applications , infrastructure-as-code (IaC) tools, and building reliable CI/CD pipelines . You will work at the intersection of data engineering, machine learning operations (MLOps), and cloud infrastructure —enabling teams to build and scale next-generation AI solutions.
Design, deploy, and maintain containerized environments using Docker and Kubernetes for hosting AI and ML workloads.
Develop and manage CI/CD pipelines for model deployment and infrastructure provisioning using tools like Terraform , Ansible , and Git .
Monitor and fine-tune the performance of distributed systems and AI frameworks such as:
Apache Spark
Apache Kafka
HBase
Cassandra
Work closely with data scientists, ML engineers, and platform teams to streamline deployment workflows and ensure infrastructure reliability.
Automate operational tasks and infrastructure management with a DevOps-first mindset.
Ensure system security, high availability, and fault-tolerance across environments.
Troubleshoot issues in real-time, performing root cause analysis and implementing fixes in production systems.
5+ years of experience in DevOps or Site Reliability Engineering roles.
Strong hands-on experience with Docker and Kubernetes in production environments.
Proven experience with Infrastructure as Code (IaC) using Terraform and Ansible .
Proficient with Git and modern DevOps practices for CI/CD.
Experience with big data / distributed systems including Spark , Kafka , HBase , or Cassandra .
Strong understanding of system monitoring, performance tuning, and alerting for large-scale applications.
Familiarity with both cloud-native tools and on-premise infrastructure .
Excellent scripting and automation skills (e.g., Bash, Python, Go).
Experience supporting ML model pipelines or MLOps frameworks (e.g., MLflow, Kubeflow, SageMaker).
Exposure to cloud platforms like AWS, Azure, or GCP.
Knowledge of data versioning tools like DVC .
Certification in Kubernetes or a relevant cloud provider.
Strong communication and collaboration skills across cross-functional teams.
Analytical mindset with problem-solving aptitude in production systems.
Self-starter attitude with the ability to take ownership of complex problems.
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