Machine Learning Engineer

ZS Associates - San Francisco, CA

Full-time - Mid Level

Remote - San Francisco, CA

Professional, Scientific, and Technical Services

About the position

The Machine Learning Engineer at ZS will play a crucial role in building and enhancing AI-enabled data products and solutions. This position focuses on developing scalable machine learning algorithms, implementing ML Ops, and ensuring high-quality deliverables through best practices in coding and architecture. The role involves collaboration with client teams and global development teams to deliver impactful projects in the healthcare sector and beyond.

Responsibilities

• Build, orchestrate, and monitor model pipelines including feature engineering, inferencing, and continuous model training.
• Scale machine learning algorithms to work on massive data sets and strict SLAs.
• Build & enhance ML Engineering platforms and components.
• Implement ML Ops including model KPI measurements, tracking, data and model drift & model feedback loop.
• Write production-ready code that is easily testable, understood by other developers, and accounts for edge cases and errors.
• Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, and periodic design/code reviews.
• Collaborate with client teams and global development team to successfully deliver projects.
• Use bug tracking, code review, version control, and other tools to organize and deliver work.
• Participate in scrum calls, and effectively communicate work progress, issues, and dependencies.
• Consistently contribute to researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts, and creating prototype solutions.

Requirements

• Bachelor's/Master's degree with specialization in Computer Science, MIS, IT or another computer related discipline.
• 2+ years' experience in deploying and productionizing ML models.
• Strong programming expertise in Python / PySpark.
• Experience in ML platforms like Dataiku, Sagemaker, MLFlow or other platforms.
• Experience in deploying models to cloud services like AWS, Azure, Google Cloud Platform.
• Expertise in crafting ML Models for high performance and scalability.
• Experience in implementing feature engineering, inferencing pipelines, and real-time model predictions.
• Experience in ML Ops to measure and track model performance.
• Good fundamentals of machine learning and deep learning.
• Knowledgeable of core Computer Science concepts such as common data structures, algorithms, and design patterns.
• Excellent oral and written communication skills.

Nice-to-haves

• Experience with Spark or other distributed computing frameworks.
• Understanding of DevOps, CI/CD, data security, experience in designing on cloud platforms.
• Experience in data engineering in Big Data systems.

Benefits

• Comprehensive total rewards package including health and well-being, financial planning, annual leave, personal growth and professional development.
• Robust skills development programs and multiple career progression options.
• Flexible and connected way of working, allowing a combination of work from home and on-site presence.