Principal Software Engineer - AI Platform

Key Responsibilities:

As a AI Ops  Expert , Responsible and full ownership for the deliverables with greater defined quality standards with defined timeline and budget   

Responsibilities:

·       Design, implement, and manage AIops solutions to automate and optimize AI/ML workflows.

·       Collaborate with data scientists, engineers, and other stakeholders to ensure seamless integration of AI/ML models into production.

·       Monitor and maintain the health and performance of AI/ML systems.

·       Develop and maintain CI/CD pipelines for AI/ML models.

·       Implement best practices for model versioning, testing, and deployment.

·       Troubleshoot and resolve issues related to AI/ML infrastructure and workflows.

·       Stay up-to-date with the latest AIops, MLOps, and Kubernetes tools and technologies.

 

Profile

 

Requirements and skills

·       Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.  

·       2-7 year relevant experience 

·       Proven experience in AIops, MLOps, or related fields.

·       Strong proficiency in Python and experience with FastAPI.

·       Strong handson expertise on Kubernetes (Or AKS)

·       Hands-on experience with MS Azure and its AI/ML services, including Azure ML Flow.

·       Proficiency in using DevContainer for development.

·       Knowledge of CI/CD tools such as Jenkins, GitHub Actions, or Azure DevOps.

·       Experience with containerization and orchestration tools like Docker and Kubernetes.

·       Strong problem-solving skills and the ability to work in a fast-paced environment.

·       Excellent communication and collaboration skills.

Preferred Skills:

·       Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.

·       Familiarity with data engineering tools like Apache Kafka, Apache Spark, or similar.

·       Knowledge of monitoring and logging tools such as Prometheus, Grafana, or ELK stack.

·       Understanding of data versioning tools like DVC or MLflow.

·       Experience with infrastructure as code (IaC) tools like Terraform or Ansible.

·       Proficiency in Azure-specific tools and services, such as:

·       Azure Machine Learning (Azure ML)

·       Azure DevOps

·       Azure Kubernetes Service (AKS)

·       Azure Functions

·       Azure Logic Apps

·       Azure Data Factory

·       Azure Monitor and Application Insights