DevOps Engineer
2 weeks ago
About the Role
We are looking for a skilled
DevOps Engineer with strong MLOps expertise
to join our team. The ideal candidate will have hands-on experience in
AWS Cloud, scripting, and automation
, along with proven skills in
deploying and managing machine learning pipelines in production
.
Key Responsibilities
- Design, implement, and maintain
CI/CD pipelines
for ML model training, testing, and deployment. - Manage
containerization & orchestration
using Docker and Kubernetes. - Automate infrastructure provisioning using
Terraform/CloudFormation
. - Implement and manage
MLOps tools
(Kubeflow, MLflow, Airflow, SageMaker, etc.). - Ensure
scalability, reliability, and monitoring
of ML/AI production systems. - Work closely with
data scientists and ML engineers
to operationalize ML models. - Develop
automation scripts
in Python/Bash to streamline workflows. - Implement and manage
AWS cloud services
(EC2, S3, EKS/ECS, Lambda, RDS, IAM, Secrets Manager). - Ensure compliance with
security, cost optimization, and performance best practices
.
Required Skills & Experience
- 7+ years
of experience as a DevOps/MLOps Engineer. - Strong hands-on expertise in
AWS Cloud
(compute, storage, serverless, IAM). - Solid knowledge of
CI/CD tools
(GitLab CI/CD, Jenkins, ArgoCD). - Proficiency in
Docker & Kubernetes
for container orchestration. - Experience with
MLOps tools
: Kubeflow, MLflow, Airflow, SageMaker, or similar. - Strong scripting/programming skills in
Python and Bash
. - Experience with
monitoring/logging tools
(Prometheus, Grafana, ELK stack). - Familiarity with
data version control
(DVC, Git) and
feature stores
is a plus. - Excellent troubleshooting and problem-solving skills.
Good to Have
- Experience with
multi-cloud (GCP/Azure ML)
. - Familiarity with
TorchServe, BentoML, KFServing
for model serving. - Knowledge of
security & compliance in ML pipelines
.
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