AI/ML DevOps Engineer, AS

Your key responsibilities

• Manage Incident, Service, Problem and Change Management of Shared AI Platforms
• Monitor production AI/ML models for performance, latency, accuracy, data drift and model drift, and proactively troubleshoot production issues.
• Automate Model Packaging, versioning and rollbacks.
• Monitor model inference speed, latency and accuracy.
• Optimize resource allocation for cost-effective AI workloads.
• Detect and mitigate data drift affecting model performance.
• Troubleshoot model failures, latency issues and deployment errors.
• Collaborate with L3 engineers and data scientists for escalations.
• Utilize containerization technologies like Docker to package models and dependencies.
  Continuous Integration/Continuous Deployment (CI/CD):
  • Develop and maintain CI/CD pipelines for automating the testing, integration, and deployment of ML models.
  • Implement version control to track changes in both code and model artifacts.
    Monitoring and Logging:
  • Establish monitoring solutions to track the performance and health of deployed models.
  • Set up logging mechanisms to capture relevant information for debugging and auditing purposes.
• Optimize ML infrastructure for scalability and cost-effectiveness.
• Implement auto-scaling mechanisms to handle varying workloads efficiently.
• Enforce security best practices to safeguard both the models and the data they process.
• Ensure compliance with industry regulations and data protection standards.
• Oversee the management of data pipelines and data storage systems required for model training and inference.
• Implement data versioning and lineage tracking to maintain data integrity.
• Collaborate with DevOps teams to align MLOps practices with broader organizational goals.
• Continuously optimize and fine-tune ML models for better performance.
• Identify and address bottlenecks in the system to enhance overall efficiency.
• Maintain clear and comprehensive documentation of MLOps processes, infrastructure, and model deployment procedures.
• Document best practices and troubleshooting guides for the team.

Your skills and experience

• Excellent communication and presentation skills, highly organized and disciplined.
• Experienced in working with multiple stakeholders. Ability to create and naturally maintain good business relationships with all stakeholders.
• Comfortable working in VUCA (Volatility Uncertainty Complexity Ambiguity) and highly dynamic environments.
• Expertise on the products/technologies below is required:
  • Google Cloud – GKE, Terraform, IAM, BigQuery, Cloud Shell, Cloud Storage
  • AI/ML – AI Agents, AI concepts, ML models, AI/ML Concepts, Vertex AI, AutoML, BigQuery ML.
  • MLOps & CICD Pipelines, Kubeflow, Vertex AI pipelines
  • Proficiency in Designing, deploying and managing AI agents e..g chatbot, virtual assistants
  • GCP Networking, Networking protocols, Security concepts, VPC, Load balancers
• Unix servers very basic administration
• Python, Shell Scripting, SQL
• Familiarity with fine-tuning and deploying large language models on GCP.
• Understanding of security best practices, including data governance, encryption, and compliance with AI-related regulations.
• GCP - Cloud Logging, Cloud Monitoring and AI Model Performance Tracking.
• 4+ years of work experience in IT; (for AVP – 6+, Associate – 4+)
• Strong problem-solving skills and a passion for AI research
• Good inter-personal skills with ability to co-operate and collaborate with other teams

Educational Qualifications:

• B.E. / B. Tech. / master’s degree in computer science or equivalent
• Added advantage. –
  • GCP Certifications
  • Kubernetes Certifications
  • AI/Ml Educational background or Certifications or higher qualifications.

How we’ll support you

• Training and development to help you excel in your career
• Coaching and support from experts in your team
• A culture of continuous learning to aid progression
  • A range of flexible benefits that you can tailor to suit your needs