MLOps Engineer

Job Title: MLOPS Support Engineer – ServiceOps

Role Band: VII

Location: GCC Bangalore

Reporting to: Team Lead – BrewDat Service Ops

Job Summary:

As an MLOps Support Engineer you will play a crucial role in ensuring the reliability performance and scalability of machine learning operations (MLOps) within our organization. You will work closely with data scientists MLOps engineers and other technical teams to provide expert support and troubleshooting assistance for machine learning infrastructure tools and workflows. Your primary focus will be on diagnosing and resolving issues optimizing processes and implementing best practices to enhance the efficiency of our machine learning systems.

Responsibilities:

1. Technical Support:

• Serve as the first point of contact for technical issues related to machine learning infrastructure pipelines and deployments.
• Respond to support tickets emails and inquiries in a timely manner and provide accurate and actionable solutions to users and stakeholders.
• Collaborate with crossfunctional teams to address complex technical challenges and ensure timely resolution of issues.

1. Incident Management:

• Monitor system health and performance metrics to proactively identify and address potential issues before they impact operations.
• Investigate and troubleshoot incidents including root cause analysis impact assessment and resolution implementation.
• Escalate critical issues to appropriate teams and stakeholders and participate in incident response and postincident reviews.

1. Documentation and Knowledge Sharing:

• Maintain comprehensive documentation of support procedures troubleshooting guides and best practices to facilitate knowledge sharing and training.
• Contribute to internal knowledge bases wikis and forums to ensure that support resources are up to date and accessible to all relevant parties.

1. Process Optimization:

• Identify opportunities to streamline and automate support processes workflows and tooling to improve efficiency and scalability.
• Work closely with MLOps engineers and DevOps teams to implement automation scripts monitoring solutions and selfservice tools for common support tasks.

1. Training and Education:

• Provide training and guidance to users developers and stakeholders on MLOps best practices tools and workflows.
• Conduct workshops webinars and tutorials to help internal teams develop the skills and knowledge required to effectively utilize machine learning infrastructure and tools.

1. Continuous Improvement:

• Collect feedback from users and stakeholders to identify areas for improvement in machine learning systems processes and user experience.
• Propose and implement enhancements updates and optimizations to meet evolving business requirements and industry standards.

Qualifications:

• 5 to 8 years of experience with bachelor’s degree in computer science Engineering or a related field.
• Proven experience in a technical support or operations role preferably in a datadriven or machine learning environment.
• Strong understanding of machine learning concepts algorithms and workflows.
• Proficiency in scripting and automation using language such as Python
• Experience with containerization technologies (Docker Kubernetes) SQL Airflow Azure Databricks APIs Arize AI DataDog version control system Git Azure cloud Compute Services Storage Services Networking Services Identity Access Management Security & Compliance.
• Excellent analytical and problemsolving skills with the ability to troubleshoot complex technical issues and perform root cause analysis.
• Strong communication and interpersonal skills with the ability to collaborate effectively with diverse teams and stakeholders.
• Selfmotivated and proactive attitude with a commitment to continuous learning and professional development.

Preferred:

• Azure Certifications: Azure AI Engineer Associate Azure Data Scientist Associate

Azure AI Fundamentals

• Experience with MLOps tools and frameworks (e.g. MLflow TensorFlow Extended).
• Experience with ML algorithms Data Preparation model monitoring (Data drift & Model performance) and retraining.
• Knowledge of data engineering concepts and tools for data preprocessing and feature engineering.
• Familiarity with DevOps practices and configuration management.
• Understanding of security best practices for machine learning systems and data privacy regulations.