Senior Machine Learning Engineer

at  Persistent Systems

PREFERRED SKILLS:

• Experience collaborating with data science teams and understanding their needs/challenges
• Ability to lead initiatives and communicate effectively with technical teams and senior leadership
• Proven ability to understand business problems and identify technical solutions
• Familiarity with ML tools and frameworks, including cloud-based ML Ops
• Retail experience is a plus

REQUIREMENTS

• Proven experience working collaboratively with data science teams, addressing their needs and challenges
• Strong engineering skills with a focus on MLOps, ensuring effective management of the entire ML workflow
• Expertise in Google Cloud Platform (GCP) and its services for machine learning operations
• Ability to lead initiatives and effectively communicate with both technical teams and senior leadership
• Proven ability to understand complex business problems and identify practical technical solutions
• Familiarity with a range of ML tools and frameworks, with an openness to adopting emerging technologies

• Maintain expertise in a range of ML technologies and platforms, with a preference for Google Vertex AI, while remaining open to other systems as needed
• Leverage open-source frameworks like TensorFlow, PyTorch, and scikit-learn, integrating them with ML frameworks via custom containers
• Stay updated on the latest trends in MLOps and ML technologies
• Apply hands-on experience to design and develop recommender systems using ML techniques such as embedding-based retrieval, reinforcement learning, transformers, and LLMs
• Utilize software engineering skills to integrate recommender systems into customer-facing products
• Conduct A/B testing and iterative optimization using data-driven approaches
• Ensure an understanding of the infrastructure needs for deploying ML systems, including CPU/GPU and networking infrastructure
• Manage, share, and reuse machine learning features at scale using Vertex AI Feature Store
• Implement feature stores as central repositories to enhance transparency in ML operations across the organization
• Enable secure feature delivery through endpoint exposure while maintaining authority and security features
• Assist with data labeling and management to ensure high-quality data for ML models
• Collaborate with data engineers and scientists to ensure data integrity and efficiency in ML model development
• Support end-to-end integration from data to AI, leveraging tools like BigTable and BigQuery to execute ML models on business intelligence tools
• Monitor ML systems in production, identify improvement opportunities, and implement optimizations
• Participate in support rotations and handle support calls as necessary