MLOps Engineer - CI/CD Pipeline

ROLE OBJECTIVE :

The MLOps Engineer position will support various segments of TE Connectivity by enhancing and optimizing the deployment and operationalization of machine learning models.

The primary objective is to collaborate with data scientists, data engineers, and business stakeholders to ensure efficient, scalable, and reliable ML model deployment and monitoring. The role involves integrating ML models into production systems, automating workflows, and maintaining robust CI/CD pipelines.

RESPONSIBILITIES :

- Model Deployment and Operationalization : Implement, manage, and optimize the deployment of machine learning models into production environments.

- CI/CD Pipelines : Develop and maintain continuous integration and continuous deployment pipelines to streamline the deployment process of ML models.

- Infrastructure Management : Design and manage scalable, reliable, and secure cloud infrastructure for ML workloads using platforms like AWS and Azure.

- Monitoring and Logging : Implement monitoring, logging, and alerting mechanisms to ensure the performance and reliability of deployed models.

- Automation : Automate ML workflows, including data preprocessing, model training, validation, and deployment using tools like Kubeflow, MLflow, and Airflow.

- Collaboration : Work closely with data scientists, data engineers, and business stakeholders to understand requirements and deliver solutions.

- Security and Compliance : Ensure that ML models and data workflows comply with security, privacy, and regulatory requirements.

- Performance Optimization : Optimize the performance of ML models and the underlying infrastructure for speed and cost-efficiency.

EXPERIENCE :

- Years of Experience : 4-6 years of experience in ML model deployment and operationalization.

- Technical Expertise : Proficiency in Python, Azure ML, AWS Sagemaker, and other ML tools and frameworks.

- Cloud Platforms : Extensive experience with cloud platforms such as AWS and Azure Cloud Platform.

- Containerization and Orchestration : Hands-on experience with Docker and Kubernetes for containerization and orchestration of ML workloads.

PROFESSIONAL EXPERIENCE :

- Model Development and Deployment : Expertise in developing, deploying, and maintaining machine learning models in production environments.

- Data Handling : Strong knowledge of data extraction, cleansing, preparation, and integration from various sources.

- CI/CD and Automation : Proficient in setting up and managing CI/CD pipelines and automating ML workflows.

- Collaboration : Proven ability to work collaboratively with cross-functional teams and stakeholders.

SPECIAL QUALIFICATIONS/ SKILLS

- Statistical Tools : Proficient in statistical tools and libraries such as Python, Azure Machine Learning, and Auto ML tools.

- Analytical Projects : Experience in end-to-end analytical project implementation on cloud platforms.

- Innovative Thinking : Ability to work independently with minimal supervision, demonstrating accountability, high work quality, and innovative thinking.

- Tool/Technique Knowledge : Up-to-date knowledge of new tools and techniques in the analytical field.

EDUCATION/KNOWLEDGE :

- Educational Qualification : Master's degree (preferably in Computer Science) or B.Tech / B.E.

- Domain Knowledge : Familiarity with EMEA business operations is a plus.

OTHER IMPORTANT NOTES :

- Flexible Shifts : Must be willing to work flexible shifts.

- Team Collaboration : Experience with team collaboration and cloud tools.

- Algorithm Building and Deployment : Proficiency in building and deploying algorithms using Azure/AWS platforms.