Machine Learning DevOps Engineer - AWS/Python (7-11 yrs)

bout the Role :

We are seeking a highly skilled and experienced Machine Learning DevOps Engineer to join our dynamic team. You will be a key player in building and maintaining the infrastructure and processes that enable us to develop, deploy, and scale machine learning models efficiently and reliably.

You will work closely with machine learning engineers, data scientists, and DevOps engineers to automate ML workflows, optimize model deployment, and ensure the performance and stability of our ML systems.

This role requires a deep understanding of both machine learning and DevOps principles, as well as hands-on experience with relevant technologies.

Responsibilities :

- Design, build, and maintain robust and scalable ML infrastructure.

- Automate ML workflows, including data preprocessing, model training, and model deployment.

- Implement CI/CD pipelines for machine learning models.

- Monitor and optimize the performance of ML systems in production.

- Ensure the reliability, security, and scalability of ML infrastructure.

- Collaborate with machine learning engineers and data scientists to streamline the ML development lifecycle.

- Develop and maintain tools and scripts for ML operations.

- Troubleshoot and resolve issues related to ML infrastructure and deployments.

- Stay up-to-date with the latest advancements in MLOps and cloud technologies.

- Champion best practices for ML DevOps within the organization.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

- 7-11 years of relevant experience in DevOps or a related field, with a focus on machine learning.

- Strong understanding of machine learning principles and techniques.

- Proficiency in at least one programming language : Python (required).

- Extensive experience with cloud computing platforms : AWS, GCP, or Azure.

- Deep understanding of containerization and orchestration technologies : Docker, Kubernetes.

- Experience with CI/CD tools : Jenkins, GitLab CI, CircleCI, or similar.

- Experience with infrastructure-as-code tools : Terraform, CloudFormation, or similar.

- Strong understanding of monitoring and logging tools : Prometheus, Grafana, ELK stack, or similar.

- Excellent problem-solving and analytical skills.

- Strong communication and collaboration skills.

Technical Skills (Required) :

- Programming Languages : Python

- Cloud Computing : AWS (SageMaker, EKS, S3, EC2, Lambda), GCP (Vertex AI, GKE, Cloud Storage, Compute Engine), or Azure (Azure Machine Learning, AKS, Blob Storage, Virtual Machines)

- Containerization/Orchestration : Docker, Kubernetes

- CI/CD : Jenkins, GitLab CI, CircleCI, GitHub Actions

- Infrastructure as Code : Terraform, CloudFormation, ARM Templates

- Monitoring/Logging : Prometheus, Grafana, ELK stack, CloudWatch, Stackdriver, Azure Monitor

- MLOps Tools : MLflow, Kubeflow, TFX, or similar

- Operating Systems : Linux-

Version Control : Git

Preferred Qualifications :

- Experience with big data technologies : Spark, Hadoop.

- Experience with message queues : Kafka, RabbitMQ.

- Experience with databases : SQL and NoSQL databases.

- Experience with machine learning frameworks : TensorFlow, PyTorch, scikit-learn.

- Knowledge of security best practices for ML systems.

- Experience with performance tuning and optimization of ML workloads