Ai/ml Operations

3 days ago

Pune Maharashtra, India InfraCloud Technologies Full time

**Job Overview**:
**Key Responsibilities**:

- Model Development & Training.
- Develop and implement efficient pipelines for training, validating, and fine-tuning machine learning models.
- Ensure that models are trained on relevant datasets and are capable of handling a wide variety of use cases
- Deployment and Infrastructure:

- Design and implement the architecture required for self-hosted AI models, both on-premise and in the cloud, depending on project requirements.
- Work closely with cloud infrastructure teams (AWS, GCP, Azure) to deploy and scale models in a production environment.
- Set up automated deployment pipelines for the continuous integration and delivery (CI/CD) of machine learning models.
- Model Monitoring and Performance Tuning:

- Monitor model performance and identify areas for improvement, including latency, resource consumption, and prediction accuracy.
- Develop strategies for model retraining, continuous learning, and tuning to maintain the best performance over time.
- Implement logging and monitoring systems to track model health and trigger alerts for potential issues.
- Integration & Automation:

- Integrate AI models with the existing platform, ensuring smooth interaction between models and front-end systems.
- Automate the end-to-end ML pipeline, from data collection and preprocessing to model deployment and feedback loops.
- Collaborate with DevOps and infrastructure teams to ensure smooth operation of models in production environments.
- Collaboration & Documentation:

- Document model architectures, training processes, deployment pipelines, and system configurations for internal use.
- Provide technical support and troubleshooting for model-related issues during integration or in production.
- Security and Compliance:

- Ensure compliance with data privacy and security regulations in the AI model deployment process.
- Implement robust access controls, encryption, and audit logging for model and data access.

**Qualifications**:

- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- Experience:
- 3+ years of experience in MLOps, with a focus on machine learning model deployment and management.
- Hands-on experience with both on-premise and cloud-based AI model deployment (AWS, GCP, Azure).
- Experience with containerization technologies (Docker, Kubernetes) for model deployment and scaling.
- Familiarity with continuous integration/continuous delivery (CI/CD) tools (e.g., Jenkins, GitLab CI, CircleCI).
- Experience in monitoring tools (e.g., Prometheus, Grafana) and managing model performance at scale.

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