Responsibilities include:
• MLOps Strategy: Develop and implement MLOps strategies, best practices, and standards to
enhance AI ML model deployment and monitoring efficiency. Develop roadmap and strategy for
MLOps and LLMOps Platforms and model lifecycle implementation
• ML Architecture Design and Development: Responsible for the design and development of
custom architecture for batch and stream processing-based AI ML pipelines including data
ingestion to preprocessing to scaled AI model compute and ensure the architecture meets all SLA
requirements. Work closely with members of technology and business teams in the design,
development, and implementation of Enterprise AI platform.
• Infrastructure Management: Oversee the design, deployment, and management of scalable and
reliable infrastructure for AI, ML , GenAI, LLM model training and deployment.
• Model Deployment: Lead the deployment of GenAI, LLM , machine learning models into
production environments, ensuring reliability and scalability.
• Monitoring and Optimization: Create and maintain robust monitoring systems to track model
performance, data quality, and infrastructure health. Identify and implement optimizations to
improve system efficiency.
• Automation: Develop and maintain automated pipelines for model training, testing, and
deployment, optimizing for speed and reliability. Ensure CI-CD best practices are followed.
• Internal Collaboration: Collaborate closely with data scientists, machine learning
engineers, and software engineers to ensure smooth integration of machine learning models into
production
systems.
• Stakeholder Engagement and Collaboration: Collaborate closely with business and PM
stakeholders in roadmap planning and implementation efforts and ensure technical milestones
align with business requirements.
• Security and Compliance: Implement security measures and compliance standards to protect
sensitive data and ensure adherence to industry regulations.
• Mentorship: Recruit, develop and