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About the Role :

We are seeking a highly motivated and experienced MLOps Engineer to build and manage our document scanning and intelligence pipeline across both private cloud and Azure Cloud environments.

This role is crucial for developing and maintaining a robust, scalable, and cost-effective solution that leverages cutting-edge Large Language Models (LLMs), primarily open-source, for document processing and analysis.

You will be responsible for the entire lifecycle of model deployment, retraining, management, monitoring, and alerting, ensuring optimal performance and cost efficiency.

Responsibilities :

Pipeline Development and Deployment :

- Design, develop, and deploy the end-to-end document scanning and intelligence pipeline on both private cloud infrastructure and Azure Cloud.

- This includes integrating various components like document scanners, OCR engines, LLMs, and data storage solutions.

LLM Model Management :

- Deploy, fine-tune, retrain, and manage open-source LLMs for document understanding, information extraction, and other related tasks.

- This includes experimenting with different model architectures and hyperparameter tuning to optimize performance.

Infrastructure Management :

- Work closely with infrastructure teams to ensure the private cloud environment is suitable for hosting and running the LLM-based pipeline.

- Manage and optimize resource allocation on Azure Cloud to minimize costs.

MLOps Practices :

- Implement and maintain MLOps best practices for model versioning, experiment tracking, automated testing, and continuous integration/continuous deployment (CI/CD).

- Monitoring and Alerting: Set up comprehensive monitoring and alerting systems to track model performance, identify potential issues, and ensure pipeline stability.

- Proactively address performance bottlenecks and system failures.

Cost Optimization :

- Develop and implement strategies to optimize the cost of running the pipeline on both private and Azure cloud environments.

- This includes resource optimization, cost analysis, and exploring cost-effective solutions.

Collaboration :

- Collaborate closely with data scientists, engineers, and other stakeholders to understand requirements, provide technical guidance, and ensure seamless integration of the pipeline into existing systems.

Documentation :

- Maintain comprehensive documentation for the pipeline architecture, deployment procedures, and model management processes.

Required Skills and Experience :

Education : Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.

Experience :

- 3+ years of experience in MLOps engineering, with a focus on deploying and managing machine learning models in production environments.

- Strong experience with cloud platforms, specifically Azure and private cloud infrastructure.

- Experience with containerization technologies like Docker and Kubernetes is highly desirable.

- Familiarity with Large Language Models, particularly open-source models, and experience with fine-tuning and deploying them.

- Proficiency with MLOps tools and frameworks such as MLflow, Kubeflow, or similar.

- Strong programming skills in Python and other relevant languages.

- Experience with data processing and ETL pipelines.

- Experience with setting up monitoring and alerting systems using tools like Prometheus, Grafana, or similar.

Preferred Skills :

- Experience with document scanning and OCR technologies.

- Knowledge of information extraction techniques.

- Experience with cost optimization strategies in cloud environments.

- Familiarity with Agile development methodologies