Machine Learning Engineer

ZS Associates - Philadelphia, PA

Full-time - Mid Level

Remote - Philadelphia, PA

10,001+ employees

Professional, Scientific, and Technical Services

About the position

ZS is a management consulting and technology firm focused on transforming global healthcare and beyond. As a Machine Learning Engineer in the AI Practice, you will be part of a team that is building transformative AI-enabled data products and solutions. The ZS suite of products includes hyper-personalization, customer journey design, AI-guided selling, large-scale unstructured customer data mining with NLP, and dynamic pricing. You will work with state-of-the-art machine learning and deep learning techniques, as well as ML engineering platforms to deliver impactful solutions for clients. In this role, you will be responsible for building, orchestrating, and monitoring model pipelines, which include feature engineering, inferencing, and continuous model training. You will scale machine learning algorithms to work on massive datasets while adhering to strict service level agreements (SLAs). Additionally, you will enhance ML engineering platforms and components, implement ML Ops practices, and ensure the highest quality of deliverables by following architecture and design guidelines. Collaboration with client teams and the global development team will be essential to successfully deliver projects. You will also participate in scrum calls, communicate work progress, and contribute to researching and evaluating the latest architecture patterns and technologies. This role requires a strong foundation in machine learning and deep learning, as well as excellent programming skills in Python and experience with various ML platforms. Your contributions will help drive life-changing solutions for patients, caregivers, and consumers worldwide.

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 conducting 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.
• 2-4 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, GCP.
• 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, and experience in designing on cloud platforms.
• Experience in data engineering in Big Data systems.

Benefits

• Comprehensive total rewards package including health and well-being benefits.
• Financial planning support.
• Annual leave.
• Personal growth and professional development opportunities.
• Robust skills development programs.
• Multiple career progression options and internal mobility paths.
• Flexible working arrangements combining work from home and on-site presence.