Machine Learning Specialist

RandomTrees

2 - 5 years

0 Lacs

Chennai Tamil Nadu India

Posted:9 hours ago| Platform:

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Skills Required

learning ml orchestration model deployment design monitoring apache airflow aws azure data automate engineering training integration code drift terraform containerization docker kubernetes packaging software research inference efficiency scalability troubleshooting documentation python tensorflow pytorch gcp sagemaker vertex gitlab jenkins devops database sql debugging communication collaboration agile

Work Mode

On-site

Job Type

Full Time

Job Description

About the Company

About the Role

Responsibilities

ML Model Deployment & Management
: Design, develop, and implement end-to-end MLOps pipelines for deploying, monitoring, and managing machine learning models in production.
Orchestration
: Utilize orchestration tools (e.g., Apache Airflow, Kubeflow, AWS Step Functions, Azure Data Factory) to automate ML workflows, including data ingestion, feature engineering, model training, validation, and deployment.
CI/CD for ML
: Implement Continuous Integration/Continuous Deployment (CI/CD) practices for ML code, models, and infrastructure, ensuring rapid and reliable releases.
Monitoring & Alerting
: Establish comprehensive monitoring and alerting systems for deployed ML models to track performance, detect data drift, model drift, and ensure operational health.
Infrastructure as Code (IaC)
: Work with IaC tools (e.g., Terraform, CloudFormation) to manage and provision cloud resources required for ML workflows.
Containerization
: Leverage containerization technologies (Docker, Kubernetes) for packaging and deploying ML models and their dependencies.
Collaboration
: Collaborate closely with Data Scientists, Data Engineers, and Software Developers to translate research prototypes into production-ready ML solutions.
Performance Optimization
: Optimize ML model inference and training performance, focusing on efficiency, scalability, and cost-effectiveness.
Troubleshooting & Debugging
: Troubleshoot and debug issues across the entire ML lifecycle, from data pipelines to model serving.
Documentation
: Create and maintain clear technical documentation for MLOps processes, pipelines, and infrastructure.

Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.
2-5 years of professional experience as a Machine Learning Engineer, MLOps Engineer, or a similar role.

Required Skills

Strong proficiency in Python and its ML ecosystem (e.g., scikit-learn, TensorFlow, PyTorch, Pandas, NumPy).
Hands-on experience with at least one major cloud platform (AWS, Azure, GCP) and their relevant ML/MLOps services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
Proven experience with orchestration tools like Apache Airflow, Kubeflow, or similar.
Solid understanding and practical experience with MLOps principles and best practices.
Experience with containerization technologies (Docker, Kubernetes).
Familiarity with CI/CD pipelines and tools (e.g., GitLab CI/CD, Jenkins, Azure DevOps, AWS CodePipeline).
Knowledge of database systems (SQL and NoSQL).
Excellent problem-solving, analytical, and debugging skills.
Strong communication and collaboration abilities, with a capacity to work effectively in an Agile environment.

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