Senior Machine Learning Engineer

Job Summary:
We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our innovative team on a mission to revolutionize the audit process through cutting-edge AI technology. As a Senior ML Engineer, you will play a pivotal role in designing, implementing, and maintaining scalable machine learning models that drive our AI Agents, ensuring seamless integration within our cloud-based infrastructure. Your expertise will bridge the gap between theoretical data science and practical software engineering, enabling our AI-driven solutions to achieve unprecedented efficiency and accuracy.

Key Responsibilities:
- Model Deployment & Management: Deploy, monitor, and manage machine learning models within our cloud infrastructure, ensuring high availability and performance.
- Collaboration & Integration: Work closely with data scientists to transition experimental models into production, optimizing them for scalability and efficiency.
- Infrastructure Development: Develop and maintain the infrastructure required for optimal extraction, transformation, and loading of data from various sources using SQL and Azure technologies.
- Continuous Improvement: Continuously evaluate existing models and infrastructure for improvements, applying the latest in machine learning research and technology.
- Best Practices Implementation: Implement best practices for ML lifecycle management, including version control, testing, and deployment using tools like MLflow, Docker, and Kubernetes.
- Cross-Functional Teamwork: Collaborate with product managers, full-stack engineers, and UX/UI designers to ensure the ML models are well-integrated into the product, enhancing user experience and product value.

Required Skills & Qualifications:
- Experience: 5+ years of experience in machine learning engineering or a similar role, with a proven track record of deploying machine learning models in a production environment.

 Technical Proficiency:
- Expertise in Python and SQL.
- Strong experience with Docker, Kubernetes, and Azure ML or similar cloud ML services.
- Familiarity with ML lifecycle tools like MLflow.
- Experience with CI/CD tools for ML models, such as Azure Pipelines or GitHub Actions.
- Knowledge of TensorFlow Serving, TorchServe, or similar model serving frameworks.
- Problem-Solving: Exceptional problem-solving skills and the ability to work on complex systems in a fast-paced environment.
- Communication: Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team.