Machine Learning Engineer

Description

Minimum 6 years of MLOps experience, proficient in various ML platforms
Strong Python skills and familiarity with data science methodologies
Experience with Google Cloud and Vertex AI, adaptable to other cloud technologies or open-source tools
Excellent communication skills bridging technical and business domains
Experience developing advanced techniques for recommender systems
Hands-on experience with ML techniques like embedding-based retrieval, reinforcement learning, transformers, and LLMs
Proficient in software engineering to lead recommender system model lifecycle

Responsibilities

• Collaborate with data scientists to understand their needs and integrate their models into production systems efficiently.
• Act as a liaison between data science, MLOps, and leadership teams to facilitate communication and goal alignment.
• Develop, maintain, and manage scalable MLOps pipelines, particularly leveraging Google Vertex AI.
• Implement and manage Google Vertex AI's AutoML for high-quality machine learning models.
• Utilize Vertex AI Pipelines for streamlined operations and continuous modeling experiences.
• Maintain expertise in ML technologies and platforms, including TensorFlow, PyTorch, scikit-learn, and integrate them with ML frameworks.
• Work with various machine learning models such as vision, video, translation, and natural language processing.
• Efficiently manage, share, and reuse machine learning features at scale using Vertex AI Feature Store.
• Implement feature stores as a central repository for maintaining transparency in ML operations.
• Enable feature delivery with endpoint exposure while ensuring authority and security features are maintained.
• Assist with data labeling and management to ensure high-quality data for ML models.
• Collaborate with data engineers and data scientists to ensure the integrity and efficiency of data used in ML models.
• Ensure end-to-end integration for data to AI, including the use of BigTable/BigQuery for executing machine learning models on business intelligence tools.
• Monitor ML models in production, identify improvement opportunities, and implement optimizations.
• Stay updated with the latest trends in MLOps and ML technologies