MLOps Engineer - Deployment & Monitoring (6-10 yrs)

Job Description :

As a Machine Learning Operations Engineer, you will play a crucial role in bridging the gap between machine learning development and production. You will work closely with data scientists, software engineers, and IT teams to ensure the seamless deployment, monitoring, and scaling of machine learning models. Your expertise in DevOps, cloud infrastructure, and machine learning will be pivotal in driving the success of our ML initiatives.

Responsibilities :

- Design, implement, and maintain scalable and reliable ML infrastructure and pipelines.

- Collaborate with data scientists to deploy machine learning models to production environments.

- Automate the deployment, monitoring, and management of ML models using CI/CD tools such as Git, Jenkins, and Bash.

- Utilize Docker and container orchestration technologies to ensure consistent and reproducible ML environments.

- Manage infrastructure as code using Terraform to provision and manage cloud resources.

- Leverage AWS services (S3, Lambda, EC2, SNS/SQS, IAM) to build and maintain robust ML solutions.

- Develop and maintain scripts and tools in Python (3+) and Java (11+) to support ML workflows.

- Monitor and troubleshoot ML models in production, ensuring high availability and performance.

- Implement security best practices and ensure compliance with industry standards.

Must-Have Skills :

- Strong experience with DevOps practices and tools, including Git, Jenkins, and Bash.

- Proficiency in containerization technologies such as Docker and Kubernetes.

- Hands-on experience with Terraform for infrastructure as code.

- Minimum of 1 year of experience in machine learning operations

- Solid knowledge of AWS services, including S3, Lambda, EC2, SNS/SQS, and IAM.

- Proficiency in Python (3+) and Java (11+).

- Strong problem-solving skills and the ability to troubleshoot complex issues.

- Excellent communication and collaboration skills.

Preferred Qualifications :

- Experience with ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-Learn.

- Familiarity with MLOps tools and platforms such as MLflow, Kubeflow, or SageMaker.

- Knowledge of data engineering principles and experience with data pipelines.

- Understanding of software development lifecycle (SDLC) and agile methodologies.