AI Model Deployment & Integration:
- Deploy and manage AI/ML models, including traditional machine learning and GenAI solutions (e.g., LLMs, RAG systems).
- Implement automated CI/CD pipelines for seamless deployment and scaling of AI models.
- Ensure effective model integration into existing enterprise applications and workflows in collaboration with AI Engineers.
- Optimize AI infrastructure for performance and cost efficiency in cloud environments (AWS, Azure, GCP).
Monitoring & Performance Management:
- Develop and implement monitoring solutions to track model performance, latency, drift, and cost metrics.
- Set up alerts and automated workflows to manage performance degradation and retraining triggers.
- Ensure responsible AI by monitoring for issues such as bias, hallucinations, and security vulnerabilities in GenAI outputs.
- Collaborate with Data Scientists to establish feedback loops for continuous model improvement.
Automation & MLOps Best Practices:
- Establish scalable MLOps practices to support the continuous deployment and maintenance of AI models.
- Automate model retraining, versioning, and rollback strategies to ensure reliability and compliance.
- Utilize infrastructure-as-code (Terraform, CloudFormation) to manage AI pipelines.
Security & Compliance:
- Implement security measures to prevent prompt injections, data leakage, and unauthorized model access.
- Work closely with compliance teams to ensure AI solutions adhere to privacy and regulatory standards (HIPAA, GDPR).
- Regularly audit AI pipelines for ethical AI practices and data governance.
Collaboration & Process Improvement:
- Work closely with AI Engineers, Product Managers, and IT teams to align AI operational processes with business needs.
- Contribute to the development of AI Ops documentation, playbooks, and best practices.
- Continuously evaluate emerging GenAI operational tools and processes to drive innovation
📌 AI Operations Engineer (Pune)
🏢 PradeepIT Consulting Services
📍 Pune
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