We are looking for a proactive and detail-oriented AI OPS Engineer to support the deployment monitoring and maintenance of AI / ML models in production. Reporting to the AI Developer this role will focus on MLOps practices including model versioning CI / CD observability and performance optimization in cloud and hybrid environments.
Key Responsibilities :
- Build and manage CI / CD pipelines for ML models using platforms like MLflow Kubeflow or SageMaker.
- Monitor model performance and health using observability tools and dashboards.
- Ensure automated retraining version control rollback strategies and audit logging for production models.
- Support deployment of LLMs RAG pipelines and agentic AI systems in scalable containerized environments.
- Collaborate with AI Developers and Architects to ensure reliable and secure integration of models into enterprise systems.
- Troubleshoot runtime issues latency and accuracy drift in model predictions and APIs.
- Contribute to infrastructure automation using Terraform Docker Kubernetes or similar technologies.
Qualifications :
Required Qualifications :
35 years of experience in DevOps MLOps or platform engineering roles with exposure to AI / ML workflows.Hands-on experience with deployment tools like Jenkins Argo GitHub Actions or Azure DevOps.Strong scripting skills (Python Bash) and familiarity with cloud environments (AWS Azure GCP).Understanding of containerization service orchestration and monitoring tools (Prometheus Grafana ELK).Bachelors degree in computer science IT or a related field.Preferred Skills :
Experience supporting GenAI or LLM applications in production.Familiarity with vector databases model registries and feature stores.Exposure to security and compliance standards in model lifecycle managementRemote Work : No
Employment Type : Full-time
Key Skills
ASP.NET,Health Education,Fashion Designing,Fiber,Investigation
Experience : years
Vacancy : 1