What are the responsibilities and job description for the Senior Machine learning Engineer - MLOps & Recommender Systems position at Artmac Soft LLC?
Job Description
Job Description
Who we are :
Artmac Soft is a technology consulting and service-oriented IT company dedicated to providing innovative technology solutions and services to Customers.
Job Description :
Job Title : Senior Machine Learning Engineer MLOps & Recommender Systems
Job Type : C2C
Experience : 6 - 10 Years
Location : Alexander, Maine
Responsibilities :
- Proven experience with Google Vertex AI or equivalent ML platforms (e.g., AWS SageMaker, Azure ML).
- Strong hands-on expertise in building and deploying recommender systems using machine learning techniques such as embedding retrieval, reinforcement learning, and transformers.
- Expertise in TensorFlow, PyTorch, scikit-learn, and containerizing ML frameworks.
- Solid understanding of infrastructure requirements for deploying machine learning systems (including CPU / GPU usage, networking infrastructure).
- Proficiency in A / B testing and iterative optimization of ML models.
- Experience with feature store management (preferably Vertex AI Feature Store).
- Strong background in working with data engineers to ensure the quality, integrity, and efficiency of data.
- Proficiency with BigQuery, BigTable, or similar tools for executing machine learning models in BI environments.
- Experience with large-scale ML systems in production environments.
- Familiarity with MLOps best practices, CI / CD for ML models, and monitoring tools for production systems.
- Experience in data engineering, particularly in the management of large-scale datasets.
- Maintain expertise in a variety of ML platforms, with a preference for Google Vertex AI, while being flexible to adapt to other systems as required.
- Utilize open-source ML frameworks like TensorFlow, PyTorch, and scikit-learn, and integrate them with ML frameworks using custom containers.
- Design, develop, and optimize recommender systems utilizing ML techniques such as embedding-based retrieval, reinforcement learning, transformers, and large language models (LLMs).
- Efficiently manage and scale the reuse of machine learning features using Vertex AI Feature Store.
- Establish feature stores as a central repository to maintain transparency and governance in ML operations across the organization.
- Implement feature delivery pipelines with endpoint exposure, ensuring security and authority controls.
- Continuously monitor ML systems in production, identifying opportunities for improvement and optimization.
- Participate in support rotations and contribute to troubleshooting and resolving issues in production environments.
- Implement data-driven enhancements and optimizations for better system performance.
Qualification :
